# 3,868 Machine Learning Engineer Jobs (September 2026) > Browse verified machine learning engineer jobs. Explore 3,868 active ML Engineer roles updated daily. Apply in one click with the LiftmyCV AI Agent. Canonical HTML: https://www.liftmycv.com/jobs/machine-learning-engineer-jobs/ Markdown: https://www.liftmycv.com/jobs/machine-learning-engineer-jobs.md Site: https://www.liftmycv.com/ 3,868 Machine Learning Engineer Jobs (September 2026) Machine learning engineer roles in September 2026 commonly center on model development, data pipelines, experimentation, and production ML systems. Listings may span applied ML, MLOps, recommendation systems, NLP, computer vision, and platform-focused engineering work across remote, hybrid, and on-site teams. Create an account to explore the full job feed and auto-apply with LiftmyCV AI Agent. ## Expert Marina Galkina, Senior HR Manager, Lead Tech Recruiter, and Career Consultant https://www.linkedin.com/in/marina-galkina-148b93224/ "Machine Learning Engineer hiring in 2026 tends to split between people who can train models and people who can ship them. Teams are paying closer attention to production judgment: data pipelines, evaluation, latency, monitoring, and how a model behaves after release. Research depth still matters for some openings, but many ML Engineer roles now read closer to software engineering jobs with serious model fluency layered in." Marina's Market Take Senior HR Leader & Lead Tech Recruiter ### How to Land a Machine Learning Engineer Job in 2026 Machine learning engineer jobs in 2026 usually reward candidates who can connect model work to production systems. Your application should make the lane clear: training and evaluating models, deploying inference services, improving data pipelines, optimizing LLM or recommendation workflows, or maintaining ML infrastructure. A vague “ML projects” summary is weaker than showing the exact model type, data scale, deployment pattern, and business or product problem you handled. For applied machine learning engineer roles, emphasize shipped systems over notebooks. Include examples of model selection, feature engineering, evaluation metrics, experiment tracking, latency tradeoffs, monitoring, and retraining. If your work involved Python, PyTorch, TensorFlow, scikit-learn, Spark, Kubernetes, Airflow, or cloud ML services, place those tools near the project where you used them. Recruiters and technical screeners should be able to see how you moved from training data to a usable model endpoint. For LLM-focused machine learning engineer jobs, be precise about your role in retrieval, fine-tuning, prompt evaluation, embeddings, ranking, guardrails, or model serving. If you built RAG pipelines, mention the vector database, chunking strategy, evaluation method, and how you tested answer quality. If you worked on traditional ML, don’t force an LLM angle. Position yourself around forecasting, classification, ranking, personalization, computer vision, NLP, or anomaly detection based on the strongest evidence in your background. - **Application positioning:** Lead with 2 to 4 production ML projects, including model type, stack, evaluation metric, and deployment environment. - **Search strategy:** Separate ML engineer, applied scientist, MLOps engineer, LLM engineer, and data scientist listings so you apply where your evidence fits the job scope. - **Interview prep:** Be ready to discuss tradeoffs around data leakage, model drift, offline versus online metrics, latency, cost, and failure modes. LiftmyCV helps you find machine learning engineer jobs that match your skills, experience, and preferred work style, then auto-apply to relevant roles faster. For **machine learning engineer** roles, your resume should show that you can move models from experimentation into usable systems. Highlight Python, SQL, PyTorch, TensorFlow, scikit-learn, feature engineering, model evaluation, deployment, data pipelines, and cloud work in AWS, GCP, or Azure. If you’ve used MLflow, Airflow, Spark, Docker, Kubernetes, SageMaker, Vertex AI, or Databricks, place those tools near the projects where they were actually used. Cut coursework-heavy descriptions once you have production or applied project experience. A long list of algorithms is less useful than proof that you improved latency, reduced false positives, automated retraining, monitored drift, or shipped a recommendation, ranking, forecasting, NLP, or computer vision model. Certifications can help when they’re relevant, such as AWS Machine Learning, Google Professional Machine Learning Engineer, or Databricks credentials, but they shouldn’t replace project outcomes. - **Weak bullet:** “Built machine learning models using Python and TensorFlow.” - **Strong bullet:** “Developed a TensorFlow ranking model for product search, improved offline NDCG by 12%, containerized inference with Docker, and deployed batch scoring through Airflow on AWS.” Present each role with a clear split between data, modeling, and engineering ownership. If your experience is research-heavy, translate papers and experiments into measurable systems work, such as benchmarks, reproducible pipelines, or model serving in 2026. LiftmyCV helps you create an ATS-friendly machine learning engineer resume tailored to each job, so your skills and experience better match what employers are looking for. ### Interview prep for machine learning engineer roles Machine learning engineer interviews usually test both modeling judgment and production engineering. Prepare to explain one deployed model from your resume: the dataset, feature choices, validation method, offline metric, production metric, latency constraint, and what changed after launch. A useful 2026 example might cover reducing false positives in a classification model, improving retrieval quality, or monitoring drift after a recommendation model shipped. Expect a mix of coding screens, ML fundamentals, and system design. One common prompt is: *design a real-time fraud detection system*, then discuss data freshness, feature stores, model retraining, evaluation, and failure modes. You may also see questions on bias-variance tradeoffs, embeddings, gradient boosting versus neural networks, A/B testing, or debugging a model whose validation score is high but production performance is poor. Review your Python, SQL, data pipelines, experiment tracking, and cloud deployment details. Bring concise stories about messy labels, scaling inference, model monitoring, and tradeoffs you made when accuracy, cost, and interpretability pulled in different directions. ## Machine Learning Engineer Salary Data (September 2026) This salary section summarizes pay information from 3,868+ active Machine Learning Engineer postings, including roles focused on model development, production ML systems, and applied machine learning work. | Level | P25 | P50 | P75 | | --- | --- | --- | --- | | Average | 165112.5 | 200000 | 244812.5 | | Entry | 87587.5 | 108000 | 133562.5 | | Mid | 160000 | 190000 | 225000 | | Senior | 179830.25 | 215000 | 265031.25 | Skills: ["python","SQL","machine learning","deep learning","PyTorch","TensorFlow","scikit-learn","model training","model fine-tuning","feature engineering","data pipelines","ml pipelines","model deployment","ml infrastructure","experimentation","aws","kubernetes","system design","llms","generative ai"] ## Example jobs - **Applied Machine Learning Engineer — Destinus — Zurich, Zurich, Switzerland**: Imagine this. You are working on a precision inertial sensor where even after control and conventional compensation, a small residual error remains. We want to find out how much of that error can genuinely be predicted and removed using machine learning. As a Machine Learning Engineer, you will own that investigation. You will build learned compensation models, benchmark them against a strong classical baseline, and determine where ML delivers measurable value and where it does not. This is a hypothesis to test rigorously, not a predetermined solution. At Destinus, we are revolutionizing the defense industry with cutting-edge Unmanned Aerial Vehicles (UAVs). Our innovative technologies are designed to meet the unique demands of modern defense operations, delivering unparalleled speed, precision, and cost effectiveness. Destinus partners with government agencies and defense organizations worldwide to provide advanced solutions for mission-critical operations, enabling a new era of efficiency and technological superiority. Join us in shaping the future of defense with groundbreaking aerospace innovations. What You'll Do - Build ML models that predict residual sensor error using observable signals including temperature, thermal gradients, quadrature amplitude, drive signals, and sensor diagnostics - Define rigorous validation protocols across unseen thermal profiles and physical sensor units to demonstrate genuine generalisation - Benchmark learned approaches against a tuned classical baseline combining per-unit thermal compensation and adaptive Kalman filtering - Quantify the observability boundary and identify which errors are predictable from available measurements and which are fundamentally outside the model's reach - Train and evaluate models offline using sensor characterisation data, separating meaningful physical correlations from artefacts and overfitting - Work with FPGA and DSP engineers to translate successful approaches into lightweight, frozen models suitable for low-latency embedded deployment - Communicate results clearly, including when the evidence shows that a classical approach remains the better solution - **Lead Machine Learning Engineer — Faculty — United Kingdom - London**: As a Lead Machine Learning Engineer at Faculty, you'll define the technical direction for advanced AI/ML projects within a team focused on national security and AI safety. This leadership role involves ensuring models perform at scale, guiding complex AI deployments, and maintaining high standards in high-risk environments. You will collaborate with diverse technical teams and shape the future of human-centric AI while working to ensure that AI is secure and trustworthy. - **Lead Machine Learning Engineer — Nubank — Sao Paulo**: # **About Nu** Nu is the leading digital bank in Latin America, serving 140 million customers across Brazil, Mexico, and Colombia. The company has been leading an industry transformation by leveraging data and proprietary technology to develop innovative products and services. Guided by its mission to fight complexity and empower people, Nu caters to customers’ complete financial journey, promoting financial access and advancement with responsible lending and transparency. The company is powered by an efficient and scalable business model that combines low cost to serve with growing returns. Nu’s impact has been recognized in multiple awards, including Time 100 Most Influential Companies, Fast Company’s Most Innovative Companies, and Forbes World’s Best Banks. Visit our[Institutional Page](https://www.nu.com/2026-en) **Machine Learning Engineer at Nubank** At Nubank, Machine Learning Engineers sit at the core of how we make decisions at scale. We build, train, and deploy models that drive credit, fraud, risk, personalization decisions and a growing set of AI-native experiences for millions of customers every day. We do it with engineering rigor, statistical depth, and a deep focus on impact. Our MLEs work across the full modeling lifecycle: framing business problems as ML problems, engineering features, training and validating models, and deploying and monitoring them in production. We value small, independent teams that move fast, own their decisions end-to-end, and hold themselves to a high bar for quality and craft. Increasingly, that work also includes Generative AI and Agentic Engineering. Depending on the problem, our engineers design and build systems that combine models, tools, workflows, evaluation loops, and human oversight to solve real business tasks reliably in production. We strive for state-of-the-art ML practices that currently include a variety of technologies. While we value candidates that are familiar with them, we are also confident that engineers who are interested in joining Nubank will be able to learn from our team. - Large-scale model training and experimentation pipelines - Feature engineering and feature stores feeding both batch and real-time models - Model deployment and serving in production, with monitoring through operational and business metrics - Distributed data processing for training datasets at scale - Continuous Integration and Deployment into AWS and Kubernetes - Experiment tracking, model versioning, and reproducibility tooling - A robust data platform built on modern ETL/ELT practices ## **As a Machine Learning Engineer, you’re expected to:** - Frame ambiguous business problems as well-defined modeling problems - Design, build and validate machine learning models, ensuring statistical rigor and business relevance - Engineer and maintain features and datasets used for training and inference - Deploy and maintain ML models in both batch and real-time scenarios, integrating them with other systems and monitoring through operational and business metrics - Lead modeling projects end-to-end — from problem framing and stakeholder alignment to delivery, monitoring and iteration - Contribute to the design, documentation, maintenance and optimization of our modeling codebase, platforms and tooling - Translate business needs into modeling strategies aligned with Nubank's architecture and long-term goals - Partner with technical and business stakeholders to define strategies and deliver high-impact models - Share knowledge, mentor peers and contribute to ML and data literacy initiatives across Nubank ## **What We're Looking For** - Strong foundation in statistics, machine learning theory and modeling techniques (e.g. regression, tree-based models, deep learning) - Programming experience in Python and familiarity with ML libraries (e.g. scikit-learn, PyTorch, TensorFlow, XGBoost) - Experience training, validating, and tuning models, with solid understanding of overfitting, bias-variance tradeoff and evaluation metrics - Understanding of the ML model lifecycle, from training and evaluation to deployment and monitoring - Ability to write efficient SQL queries and work with analytical data environments - Strong communication skills to collaborate with both technical and business stakeholders - Passion for building high-quality, production-grade models ## **Nice to Have** - Experience with cloud platforms such as AWS, GCP or Azure - Familiarity with distributed systems, microservices and asynchronous architectures - Experience with feature stores, MLOps tooling and experiment tracking (e.g. MLflow, Feast, Airflow) - Knowledge of data architecture patterns (Data Lake, Data Warehouse, Data Mart) - Experience with data visualization tools (Looker, Power BI, Tableau or similar) Knowledge of software engineering best practices: testing, clean code, documentation # **Our Benefits** - Chance of earning equity at Nubank - Food/Meal Card (Vale-Refeição and/or Vale Alimentação) - Public Transportation Commuting Benefit (Vale-Transporte) - NuCare – Psychological, Financial and Legal Assistance Program - Life Insurance, Medical Plan and Dental Plan - NuLanguage – Language Course Program - Nucleo – Our learning platform - Extended Parental Leave, Daycare Allowance and Parental Consultancy - Work-from-home Allowance - Gym Partnerships - 30 days of paid vacation - Relocation Assistance Package, if applicable # **Work Model** Hybrid 2–3 times/week: Our hybrid work model brings us to the office at least twice a week, on strategic days designed to maximize team connection and collaboration. For more details, visit [building.nubank.com/nu-hybrid-work-model/](http://building.nubank.com/nu-hybrid-work-model/) Our recruitment process may involve the use of artificial intelligence–enabled tools, such as automated interview transcription and analysis, to support the evaluation process. Artificial intelligence is not used to make final hiring decisions; all decisions are made by human reviewers. - **Lead Machine Learning Engineer — ASAPP — Mountain View**: At ASAPP, our mission is simple: deliver the best AI-powered customer experience—faster than anyone else. To achieve that, we’re guided by principles that shape how we think, build, and execute. We value customer obsession, purposeful speed, ownership, and a relentless focus on outcomes. ASAPP’s AI Engineering team is seeking an enterprising, talented and curious machine learning engineer. The AI Engineering team is responsible for working closely with the research and modeling teams to create state-of-the-art NLP models for specific tasks, and deploy them in a production setting designed to serve our customers at scale. We are looking for a Machine Learning Engineer to help build and evaluate the core intelligence behind our agentic AI systems. This role will play a key part in designing and owning evaluation frameworks that ensure quality, safety, and performance across complex agentic systems. We're looking for a Lead Machine Learning Engineer to own and grow the evaluation platform that measures quality, safety, and performance across ASAPP's agentic AI systems- the infrastructure that tells us, with confidence, whether a model or agent change is actually an improvement before it reaches customers. This a hybrid role with 10-12 days of in-office presence per month to balance flexibility with collaboration. At ASAPP, our mission is simple: deliver the best AI-powered customer experience—faster than anyone else. To achieve that, we’re guided by principles that shape how we think, build, and execute. We value customer obsession, purposeful speed, ownership, and a relentless focus on outcomes. ASAPP’s AI Engineering team is seeking an enterprising, talented and curious machine learning engineer. The AI Engineering team is responsible for working closely with the research and modeling teams to create state-of-the-art NLP models for specific tasks, and deploy them in a production setting designed to serve our customers at scale. We are looking for a Machine Learning Engineer to help build and evaluate the core intelligence behind our agentic AI systems. This role will play a key part in designing and owning evaluation frameworks that ensure quality, safety, and performance across complex agentic systems. We're looking for a Lead Machine Learning Engineer to own and grow the evaluation platform that measures quality, safety, and performance across ASAPP's agentic AI systems- the infrastructure that tells us, with confidence, whether a model or agent change is actually an improvement before it reaches customers. This a hybrid role with 10-12 days of in-office presence per month to balance flexibility with collaboration. ### What you'll do Help develop the technical roadmap and architecture for the evaluation platform, from offline benchmarking to online/production monitoring of agentic and LLM-based systems. Design eval methodologies appropriate to different stages of the pipeline: golden/regression test sets, human-in-the-loop review workflows, LLM-as-judge approaches, and automated metrics for task success, safety, and hallucinations. Build the data infrastructure evaluation depends on: annotation and labeling pipelines, dataset versioning, data quality checks, and tooling that lets researchers and product teams run and interpret experiments without needing platform team help. Partner closely with Research, Product, and Platform teams to productize experiments into robust AI solutions Represent the eval platform to stakeholders outside the immediate team- set expectations on what "good" looks like for a model/agent release, and report on platform health and coverage. Stay current with advancements in ML, NLP, voice, and LLM systems, and contribute actively to technical discussions across teams. Mentor and support other engineers through design reviews, feedback, and knowledge sharing. ### What you'll need Deep, hands-on experience building and operating evaluation systems for modern ML/LLM/agentic systems- not just consuming existing eval tools. Demonstrated experience leading the technical direction of a project or small team: setting architecture, driving design reviews, and being accountable for a system's long-term health (not just shipping features). Strong architectural skills, with proven experience designing complex, data-intensive software systems and production experience with Python, AWS, Kubernetes, and/or Docker. Experience designing data pipelines for ML evaluation- labeling/annotation workflows, dataset versioning and quality control, and reproducible benchmarking. A Bachelor’s Degree in CS or other related fields Demonstrated technical mentorship of junior and mid-level engineers, driving adoption of best practices and architectural alignment for scalability and extensibility. Desire to learn, teach, and collaborate closely with cross-functional peers. ### What we'd like to see Experience building and evaluating agentic systems at scale. Experience with voice/audio quality evaluations. Production experience with LLM-centric services (e.g., inference, orchestration, evaluation, monitoring) Familiarity with large-scale ML experimentation, benchmarking, or simulation frameworks. Experience with conversational/customer-support AI domains (e.g., containment rate, conversation quality, goal completion). Knowledge of techniques for optimizing model architectures for faster inference. Experience with AWS, CI/CD, Kafka, Athena Benefits include: Competitive compensation with stock options Comprehensive medical, vision, and dental insurance 401k matching Fitness and wellness stipend Mental well-being benefits Professional learning and development stipend Parental leave, including adoptive and foster parents 3 weeks paid time off (increases with tenure) along with sick leave, bereavement and jury duty ASAPP is committed to creating a diverse environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, disability, age, or veteran status. If you have a disability and need assistance with our employment application process, please email us at careers@asapp.com to obtain assistance. #LI-SL1 #LI-Hybrid - **Lead Machine Learning Engineering — LATAM — Colombia**: LATAM is seeking a Senior AI Engineer to design, build, and deploy AI solutions using modern Large Language Models (LLMs) and Generative AI. The candidate should have strong software engineering skills and experience in scalable AI applications. Responsibilities include developing AI-powered applications, integrating AI platforms, collaborating with frontend engineers, and mentoring team members. Required qualifications include 10+ years in Software Engineering, expertise in Python, AI application development, and experience in cloud environments. This role requires effective communication, adaptability, and a proactive mindset. - **Junior Machine Learning Engineer — Recraft — London, United Kingdom**: # **About Us** Founded in the US in 2022 and now based in London, UK, Recraft is an AI tool for professional designers, illustrators, and marketers, setting a new standard for excellence in image generation. We designed a tool that lets creators quickly generate and iterate original images, vector art, illustrations, icons, and 3D graphics with AI. Over 3 million users across 190+ countries have produced hundreds of millions of images using Recraft, and we're just getting started. Join a universe of professional opportunities, develop and support large-scale projects, and shape the future of creativity. We are committed to making Recraft an essential, daily tool for every designer and setting the industry standard. Our mission is to ensure that creators can fully control their creative process with AI, providing them with innovative tools to turn ideas into reality. If you’re passionate about pushing the boundaries of AI, we want you on board! ## **About the Role** As a Junior Machine Learning Engineer at Recraft, you will have the opportunity to work on real-world AI applications, gaining hands-on experience in model development, data collection, evaluation, and production deployment. You will collaborate with research scientists, engineers, and product teams to help enhance Recraft’s AI-driven creative tools. If you are passionate about machine learning, deep learning, and AI-driven applications, this is a great opportunity to learn and contribute to impactful projects. ## **Key Responsibilities** - Assist in training, testing, and evaluating machine learning models for real-world applications. - Support data collection and processing for model development. - Conduct experiments and model evaluations, helping improve accuracy and efficiency. - Develop and train large-scale generative models, pushing the boundaries of AI capabilities. - Work closely with ML engineers and researchers to implement AI techniques into production workflows. - Stay updated with the latest trends in AI and deep learning, contributing fresh ideas to the team. ## Qualifications - Currently pursuing a Bachelor’s, Master’s, or PhD in Computer Science, Machine Learning, AI, or a related field. - Reliable Python coding skills. - Knowledge and understanding of foundational deep learning concepts, possibly in application to computer vision, NLP, or speech synthesis or recognition. - Experience with data preprocessing and model evaluation. - Familiarity with MLOps tools is a plus. - Strong analytical skills and ability to work in a collaborative, fast-paced environment. - English: B2+ (Upper-Intermediate or above), written and spoken. ## **What We Offer** - Real ML work from day one — not toy tasks, but production systems that solve actual problems. - Close mentorship from engineers and researchers who've built and shipped AI at scale. - A front-row seat to how AI products grow: you'll see the full picture, not just your corner of it. - A team that debates ideas, moves fast, and genuinely enjoys what it builds. - Full-time, on-site in London — because the best breakthroughs still happen in the same room. - Skilled Worker visa sponsorship available for the right candidate. - **Junior Machine Learning Engineer — MyFunded Futures — Plano, Texas, United States**: At My Funded Futures, we’re transforming the world of proprietary trading by giving traders the capital, tools, and community they need to succeed. We blend innovation, transparency, and performance to create opportunity — helping traders scale faster and smarter. If you’re passionate about fintech, financial markets, and data-driven growth, you’ll fit right in. Explore our open roles below and see how you can help us shape the future of funded trading. The Junior ML Engineer will support the Company's data science function by developing, validating, and maintaining machine learning models and the data pipelines behind them. This is a hands-on, applied role: you will work with real data on problems that directly shape the product and the business, and you will be expected to explain what your models do and why they can be trusted. You will partner closely with the Data Science and Analytics team and with stakeholders across the organization, translating business questions into well-defined analytical problems and presenting results in terms decision-makers can act on. This role is ideal for someone early in their career who has already built and shipped machine learning models and who wants broader exposure across modeling, analytics, and data engineering. ## Key Responsibilities - Develop, test, validate, and maintain machine learning models under the guidance of senior team members. - Build and maintain data pipelines and analytical datasets on the Company's cloud data platform. - Evaluate model performance rigorously and document assumptions, methods, and limitations. - Support statistical analysis, forecasting, and experimentation to inform business decisions. - Present technical findings clearly to non-technical audiences. - Contribute to standards for model documentation, validation, and monitoring. ## Qualifications - Bachelor's degree (or equivalent) in computer science, mathematics, engineering, or a related field, with coursework in machine learning or statistical learning. Graduate degree is a plus. - Strong Python and PySpark skills, with the ability to write clean, tested, maintainable code. - Hands-on experience with a cloud data platform (Databricks, Snowflake, Fabric, or similar) - Strong SQL, including window functions and multi-table joins. - Solid understanding of core ML concepts: cross-validation, overfitting, class imbalance, data leakage (including in time-ordered data), and choosing evaluation metrics appropriate to the problem. - Hands-on experience with: - Gradient-boosted trees (XGBoost, LightGBM) - Logistic regression, support vector machines, k-nearest neighbors - Clustering methods (k-means and others) Experience with some of the following: survival / time-to-event analysis, experiment design and causal inference, simulation and Monte Carlo methods, probability calibration, Bayesian or hierarchical modeling, model monitoring and drift detection Experience taking a model from development into a scheduled or production environment Docker, CI/CD, and workflow orchestration experience Ability to explain model behavior, including feature importance, calibration, and limitations. Ability to gather and present technical results to a non-technical audience. Proven experience as a machine learning engineer or in a similar role is a plus. Fintech, trading, or financial services background is a plus. ### **EEO Statement ** > **Equal Employment Opportunity** > My Funded Futures is an equal opportunity employer. We believe that diversity drives innovation and success. We are committed to building an inclusive environment where every team member feels valued, respected, and supported—regardless of race, color, religion, gender, gender identity, sexual orientation, national origin, age, disability, veteran status, or any other protected characteristic. ### **Pay Transparency ** > In compliance with pay transparency laws, My Funded Futures provides compensation ranges in job postings where required. Final compensation may vary based on experience, qualifications, and location. We also offer comprehensive benefits and performance-based incentives. ### **Accessibility / Accommodation Statement** > If you require assistance or an accommodation during the application process, please contact our HR team at [careers@myfundedfutures.com. ](mailto:careers@myfundedfutures.com) ### **Work Authorization ** > Applicants must be authorized to work in the applicable country without employer sponsorship. The Company does not offer visa sponsorship or immigration assistance for this position. - **Staff Machine Learning Engineer, ML Platform — Braze — Chicago, Austin, San Francisco, New York City**: At Braze, we have found our people. We’re a genuinely approachable, exceptionally kind, and intensely passionate crew. We seek to ignite that passion by setting high standards, championing teamwork, and creating work-life harmony as we collectively navigate rapid growth on a global scale while striving for greater equity and opportunity – inside and outside our organization. To flourish here, you must be prepared to set a high bar for yourself and those around you. There is always a way to contribute: Acting with autonomy, having accountability and being open to new perspectives are essential to our continued success. Our deep curiosity to learn and our eagerness to share diverse passions with others gives us balance and injects a one-of-a-kind vibrancy into our culture. If you are driven to solve exhilarating challenges and have a bias toward action in the face of change, you will be empowered to make a real impact here, with a sharp and passionate team at your back. If Braze sounds like a place where you can thrive, we can’t wait to meet you. **WHAT YOU'LL DO** Braze is seeking a Staff Machine Learning Engineer to join our Predictive and Generative AI (PGAI) team. The team's mission is to deliver a truly engaging and personalized customer experience through the creation of ML and AI enhanced marketing solutions. We run those solutions as production systems at global scale, from the distributed pipelines that train models for each customer to the high-throughput APIs that serve predictions into our messaging systems across multiple regions. You will own the platform underneath, and you will make deploying, operating, and scaling ML at Braze fast, safe, and efficient. As the Staff Engineer on the team, you will: - Identify and drive the transformative initiatives that change how the team runs ML in production, whether that's replatforming our queueing and orchestration, overhauling deployment and cloud identity, or retiring a generation of infrastructure - Build and ship at high velocity. Staff at Braze is a hands-on delivery role; you carry the most complex infrastructure initiatives yourself from design through production. Current examples include multi-region model serving fleets, the pipelines that keep hundreds of customer-specific models healthy, and the CI and deployment tooling that moves it all safely - Own the platform's technical vision and production quality bar. Set direction for how models are trained, deployed, served, and observed; lead incident response for ML systems; and drive the reliability and cost work that keeps the platform efficient at scale - Drive initiatives that span teams. Our platform builds on shared infrastructure, deployment tooling, and data systems owned with partner teams, and you carry the technical relationships with those teams - Raise the team's engineering quality through design review, code review, and production readiness for ML systems, and mentor other senior engineers and data scientists - Connect technical decisions to customer and business outcomes, and represent the team's technical perspective to product and engineering leadership **WHO YOU ARE** - 8+ years building and operating distributed systems in production, with depth in deployment and operations. You have designed services for scale and reliability, owned CI/CD and infrastructure as code, and run what you built under production load - Hands-on experience with ML workloads in production. Training pipelines, model serving, feature systems, or ML platform tooling all count; deep modeling experience is a plus rather than a requirement - A technical leader who has owned direction for a team, led multi-quarter initiatives across team boundaries, and grown senior engineers, all while keeping a high personal output - Deep working knowledge of Kubernetes and cloud infrastructure, including identity and access management, networking, and the cost profile of what you run - An effective communicator, both verbal and written, whose designs and recommendations build consensus and drive forward decision making - Bonus: - Queueing and orchestration systems such as Celery, RabbitMQ, Kafka, or Ray - ML platform tooling such as MLflow or another model registry, feature stores, or ML observability - Experience in our stack (Python, Ruby on Rails, MongoDB, Redis, Kubernetes) - Operating under compliance regimes such as SOX or HIPAA - Customer engagement, personalization, or marketing technology domain experience For candidates based in the United States, the pay range for this position at the start of employment is expected to be between $184,000 and $314,000/year, with an expected On Target Earnings (OTE) between $204,000 and $348,000/year (including bonus or commission). Your exact offer may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. In addition to cash compensation, this role qualifies for a comprehensive Total Rewards package that includes equity grants of restricted stock (RSUs) so that you will own a piece of our company. #LI-Hybrid **WHAT WE OFFER** *Braze benefits vary by location, and we encourage you to review our specific benefits offerings for each country*[*here*](https://www.braze.com/company/careers/how-we-hire#benefit-section)*. More details on benefits plans will be provided if you receive an offer of employment.* From offering comprehensive benefits to fostering hybrid ways of working, we’ve got you covered so you can prioritize work-life harmony. Braze offers benefits such as: - Competitive compensation that may include equity - Retirement and Employee Stock Purchase Plans - Flexible paid time off - Comprehensive benefit plans covering medical, dental, vision, life, and disability - Family services that include fertility benefits and equal paid parental leave - Professional development supported by formal career pathing, learning platforms, and a yearly learning stipend - A curated in-office employee experience, designed to foster community, team connections, and innovation - Opportunities to give back to your community, including an annual company-wide Volunteer Week and donation matching - Employee Resource Groups that provide supportive communities within Braze - Collaborative, transparent, and fun culture recognized as a Great Place to Work® **ABOUT BRAZE**********Braze is the leading customer engagement platform that empowers brands to Be Absolutely Engaging.™ Braze helps brands deliver great customer experiences that drive value both for consumers and for their businesses. Built on a foundation of composable intelligence, BrazeAI™ allows marketers to combine and activate AI agents, models, and features at every touchpoint throughout the Braze Customer Engagement Platform for smarter, faster, and more meaningful customer engagement. From cross-channel messaging and journey orchestration to Al-powered decisioning and optimization, Braze enables companies to turn action into interaction through autonomous, 1:1 personalized experiences. The company has repeatedly been recognized as a Leader in marketing technology by industry analysts, and was voted a G2 “Best of Marketing and Digital Advertising Software Product” in 2025. Braze was also named a 2025 Best Companies To Work For by U.S. News & World Report, a 2025 America’s Greatest Companies by Newsweek, and a 2025 Fortune Best Workplace in Technology™ by Great Place To Work®, among other accolades. Braze is also proudly certified as a Great Place to Work® in the U.S., the UK, Australia, and Singapore. The company is headquartered in New York with offices in Austin, Berlin, Bucharest, Chicago, Dubai, Jakarta, London, Paris, San Francisco, São Paulo, Singapore, Seoul, Sydney and Tokyo. **BRAZE IS AN EQUAL OPPORTUNITY EMPLOYER** At Braze, we strive to create equitable growth and opportunities inside and outside the organization. Building meaningful connections is at the heart of everything we do, and that includes our recruiting practices. We're committed to offering all candidates a fair, accessible, and inclusive experience – regardless of age, color, disability, gender identity, marital status, maternity, national origin, pregnancy, race, religion, sex, sexual orientation, or status as a protected veteran. When applying and interviewing with Braze, we want you to feel comfortable showcasing what makes you *you*. We know that sometimes different circumstances can lead talented people to hesitate to apply for a role unless they meet 100% of the criteria. If this sounds familiar, we encourage you to apply, as we’d love to meet you **OUR AI-POWERED BRAZE RECRUITMENT PROCESS** At Braze, we’re committed to a fair and transparent candidate experience. To help our recruitment teams focus on what matters most — the person behind each application — we use AI-assisted tools at certain stages of our recruitment process. This includes using AI to analyze the experience, skills and qualifications in your application materials to help with screening and prioritizing candidates. Such screening may amount to a form of solely automated decision-making. We also use AI for administrative support, like scheduling and recording interviews and summarizing interview notes. Our recruiting teams remain responsible for all hiring decisions and are involved throughout the process. Depending on where you are located, you may have certain rights available to you in relation to Braze’s use of AI: - To opt out of AI-assisted review of your application, please click the “Learn More” at the end of the application form below and follow the instructions before submitting your application. Please note, if you apply to multiple roles at Braze, you will need to opt out in relation to each application. - To exercise other types of rights, such as rights to request further information about how AI is used in our recruitment process, to request a manual review of any decision made or to contest a decision, please contact us at [talentdata.privacy@braze.com](mailto:talentdata.privacy@braze.com). Please contact us at [talentdata.privacy@braze.com](mailto:talentdata.privacy@braze.com) with any questions.****To find out more about our hiring process, check out [this page](https://www.braze.com/company/careers/how-we-hire). **Notice Regarding Automated Employment Decision Tool (NYC Local Law 144)** Our use of AI during the application review process may include the use of automated employment decision tools. Pursuant to New York City Local Law 144, for roles based in New York City, or if you reside in New York City, you have the right to request an alternative selection process or a reasonable accommodation instead of AI-assisted review. To opt out of AI-assisted review of your application, please click the “Learn More” button below and follow the instructions before submitting your application. Please note, if you apply to multiple roles at Braze, you will need to opt out in relation to each application. Please submit any other request to our Talent Acquisition team at [talentdata.privacy@braze.com](mailto:talentdata.privacy@braze.com) promptly after applying. Summaries of the most recent bias audit results for such tools are available [here](https://www.braze.com/company/legal/bias-audit-candidates)*.* *Please see our*[*Candidate Privacy Policy*](https://www.braze.com/company/legal/privacy-policy-job-candidates)*for more information on how Braze processes your personal information during the recruitment process and, if applicable based on your location, how you can exercise any privacy rights.* - **Senior Applied Machine Learning Engineer — MaintainX (An Autodesk Company) — Canada**: Join MaintainX, a leader in AI-powered maintenance and asset management, as a Senior Applied Machine Learning Engineer. You will guide the architecture of predictive maintenance initiatives and combine ML expertise with software development. Collaborate with product leaders and mentor a team to bring AI solutions to thousands of industrial sites, while working with cutting-edge technologies. Contribute to a mission that enhances the operations of frontline teams across the globe. - **Principal Machine Learning Engineer — Level AI — Bangalore**: Level AI, a conversational intelligence company based in Mountain View, seeks a Principal AI Research Engineer to guide the direction of its AI initiatives. This senior role focuses on cutting-edge research in large language models, speech understanding, and retrieval systems, transitioning research into scalable production capabilities. The ideal candidate will possess extensive experience in ML/AI, strong deep learning expertise, and the ability to mentor and influence engineering teams. - **Principal Machine Learning Engineer — Workday Limited — Ireland, Dublin**: **Your work days are brighter here.** We’re obsessed with making hard work pay off, for our people, our customers, and the world around us. As a Fortune 500 company and a leading AI platform for managing people, money, and agents, we’re shaping the future of work so teams can reach their potential and focus on what matters most. The minute you join, you’ll feel it. Not just in the products we build, but in how we show up for each other. Our culture is rooted in integrity, empathy, and shared enthusiasm. We’re in this together, tackling big challenges with bold ideas and genuine care. We look for curious minds and courageous collaborators who bring sun-drenched optimism and drive. Whether you're building smarter solutions, supporting customers, or creating a space where everyone belongs, you’ll do meaningful work with Workmates who’ve got your back. In return, we’ll give you the trust to take risks, the tools to grow, the skills to develop and the support of a company invested in you for the long haul. So, if you want to inspire a brighter work day for everyone, including yourself, you’ve found a match in Workday, and we hope to be a match for you too. **About the Team** Danu is part of Workday's AI Centre of Excellence in Dublin, operating within the AI Platform organization to support a customer base of 60 million users. Danu's charge is AI privacy — researching and translating sophisticated challenges in human-AI partnership, ML model performance, Explainable AI (XAI), and Responsible AI into production capabilities, with a specific focus on anonymization. Our de-identification engine, ogham — named for Ireland's earliest alphabet — already detects PII at industry-leading recall and efficiency at enterprise scale. We are now extending our privacy engineering capabilities to build the next chapter: a dedicated anonymization capability that will allow Workday and its customers to set new industry-leading privacy standards. If you want to lead the work that sets the benchmark for how a global AI platform protects sensitive data — responsibly and at scale — we'd like to meet you. **About the Role** As Principal Machine Learning Engineer for anonymization, you will lead Danu’s newest area: building the capability that provides anonymized datasets for customer-accessible research and benchmarking. You will drive this area from the front. Part of this work is expected to run with external research partners specialising in differential privacy and formal privacy analysis; you will lead from Workday’s side, setting the technical direction, owning the interface into those engagements, and bringing results back into the platform. You will own the architecture that turns it into a platform other teams can consume. You will drive a dedicated group of engineers hiring alongside you, and act as Workday's technical authority on anonymization with Product Legal, compliance and executive stakeholders. ## ## **Your First Six Months** You will establish how Workday measures the privacy-utility trade-off across the techniques in play — differential privacy, group anonymization (k-anonymity, l-diversity, t-closeness), and synthetic data generation — against real research use cases. That evaluation standard is what the broader platform capability is built on, and what legal, compliance, and customers are asked to trust. Architecture and roadmap follow from it, and you will own both. ### ## **Key Responsibilities** - **Technical Leadership of Anonymization:** Own the technical vision, architecture, and roadmap for Workday’s anonymization capability, building on Danu’s de-identification foundation to deliver a platform serving research, benchmarking, synthetic data generation, and agent evaluation across the AI ecosystem. - **Applied Privacy Research**: Lead applied research across these techniques and the ones that follow them, closing linkage-attack gaps and incubating approaches ahead of industry convergence. - **Hands-On Technical Depth:******Build and evaluate the anonymization models — calibrating privacy parameters against utility, and running the adversarial evaluations that test whether the guarantees hold. - **Leading the Group & Setting Standards:******Drive the anonymization group day to day — technical planning, design review, and code review. Establish the architectural patterns for anonymization, aligned with the de-identification standards Danu already operates, and define what partner teams build against when they consume anonymized data. - **Strategic Collaboration:******Partner with Product Legal, compliance, product, executive stakeholders and external partners to shape Workday’s long-term anonymization and data-governance strategy, and translate privacy-utility decisions for technical, legal, and customer-facing audiences. - **External Representation:******Represent Workday’s anonymization work externally — benchmarking against emerging privacy frameworks, engaging with the research community, and contributing to the standards the industry is still forming. **About You** You are a technical authority in machine learning with a track record of taking hard problems from research concepts to enterprise-grade production, and of leading others while staying close to engineering. You combine depth in privacy-preserving techniques with the judgement to make defensible trade-offs where the literature offers no clear answer, and the communication skill to explain those trade-offs to people who are not engineers. ## **Basic Qualifications** • ******Experience:** 10+ years of hands-on experience in Machine Learning Engineering, Data Science, or applied research, including leading technical initiatives from research through enterprise production deployment. • ******Privacy & Anonymization:******Demonstrable depth in privacy-preserving machine learning, in research or production — differential privacy, group anonymization, or synthetic data generation. Given how recently these techniques have matured, we are looking for genuine expertise rather than long tenure. • ******Core Programming & ML Stack:** Expert-level Python and modern ML frameworks such as PyTorch or TensorFlow. • ** ****Data Pipelines:******Proven experience architecting and operating large-scale data processing pipelines using Spark or equivalent distributed frameworks. • **Technical Leadership:** Track record of driving a group of engineers through ambiguous technical problems from a standing start, setting cross-team architecture standards, and mentoring mid-level and senior engineers. • **Education:******Bachelor’s degree in Computer Science, Physics, Mathematics, or a related quantitative field (or equivalent practical experience). ## ## **Other Qualifications (Nice-to-Haves / Areas to Grow)** • **Modeling & NLP:** Experience with classification, Named-Entity Recognition (NER), transformer architectures, LLM fine-tuning using the Hugging Face ecosystem, and model inference optimization for GPU hardware. • **Advanced Degree:******Master’s or Ph.D. in Computer Science, Machine Learning, Statistics, or a related quantitative discipline. • **Cloud & Production:******Hands-on experience deploying, scaling, and maintaining ML systems in production on AWS (or equivalent cloud platform). • **GenAI & Agent Systems:******Experience with agent execution, agent orchestration, or LLM evaluation frameworks (e.g. LangGraph, LangSmith), particularly where agents consume sensitive data at scale. • ******Responsible AI & Compliance:** Strong understanding of Responsible AI practices (bias/fairness evaluation) and privacy regulatory frameworks such as GDPR, with experience representing technical decisions to legal and compliance partners. • **Working with External Partners:******Experience directing external research partners, consultancies, or academic collaborators — scoping the work, assessing methodology critically, and bringing results into production systems. • **Research Visibility:******Publications, patents, open-source contributions, or conference work in privacy-preserving ML or adjacent fields. **Workday Pay Transparency Statement (For EU Locations Only)** Listed below is the base salary range applicable to this position. Workday pay ranges (and the precise pay offered to the successful candidate) are based on a number of objective criteria such as relevant experience and skills, and educational qualifications, level of responsibility, demands of the role, work location and business need. As a part of the total compensation package, this role may be eligible for the Workday Bonus Plan or a role-specific commission/bonus, as well as annual refresh stock grants awarded by Workday Inc. For more information regarding Workday’s comprehensive benefits, please [click here](http://benefits.workday.com/). Primary Location Base Pay Range: €116,000 EUR - €174,000 EUR Ireland **Our Approach to Flexible Work** With Flex Work, we’re combining the best of both worlds: in-person time and remote. Our approach enables our teams to deepen connections, maintain a strong community, and do their best work. We know that flexibility can take shape in many ways, so rather than a number of required days in-office each week, we simply **spend at least half (50%) of our time each quarter in the office or in the field** with our customers, prospects, and partners (depending on role). This means you'll have the freedom to create a flexible schedule that caters to your business, team, and personal needs, while being intentional to make the most of time spent together. Those in our remote "home office" roles also have the opportunity to come together in our offices for important moments that matter. Pursuant to applicable Fair Chance law, Workday will consider for employment qualified applicants with arrest and conviction records. Workday is an Equal Opportunity Employer including individuals with disabilities and protected veterans. Workday is committed to providing reasonable accommodations for qualified individuals during our application process, in order to perform one or more essential functions of their job, as well as regarding the use of AI tools for employment decision-making to any degree. Please see below for more details including how to request an accommodation as a qualified veteran, due to a disability or for religious reasons, or as otherwise provided under applicable law. Workday prohibits taking adverse action against any candidate or employee for reporting a possible violation of this policy, requesting one or more work accommodations, exercising a privacy right, or cooperating in an investigation in accordance with applicable law. Any employee who retaliates against a candidate or employee for doing so may be subject to disciplinary action, up to and including termination of employment, to the fullest extent allowable under applicable law. If you require a reasonable accommodation, you may email [accommodations@workday.com](mailto:accommodations@workday.com), as far in advance as possible. **Are you being referred to one of our roles? If so, ask your connection at Workday about our Employee Referral process!** At Workday, we value our candidates’ privacy and data security. Workday will never ask candidates to apply to jobs through websites that are not Workday Careers. Please be aware of sites that may ask for you to input your data in connection with a job posting that appears to be from Workday but is not. In addition, Workday will never ask candidates to pay a recruiting fee, or pay for consulting or coaching services, in order to apply for a job at Workday. - **Principal Machine Learning Engineer — Eli Lilly Services India Pvt Ltd — IN: Lilly Bengaluru**: At Lilly, the work is demanding because patients are waiting. We unite caring with discovery to help make life better for people around the world, knowing that every decision, every detail, and every day matters. Headquartered in Indianapolis, Indiana, our over 50,000 employees around the globe take on complex challenges to discover and deliver life-changing medicines, strengthen how health is understood and managed, and support the communities we serve. This is hard, urgent, selfless work—but it’s work worth doing. If you’re driven by purpose and ready to bring your best to work that truly matters for patients, we invite you to join us. ## **Role Overview** We are looking for a Principal Machine Learning Engineer to join the AI Engineering team, with a primary focus on Hands-On Engineering (MLE) and MLOps & Platform Reliability and GenAI & Agentic Systems. This posting is at level R3 on our engineering ladder — see the level framing below for the expected scope of ownership and impact. Level framing: Recognized technical expert; leads decisions on technical approach for projects; solves complex problems and innovates solutions; drives improvements across multiple projects/teams; may manage budgets. ## **Core Responsibilities** - **Design and build complex ML/AI systems and services,** setting the technical approach for your workstream. - **Solve complex, ambiguous technical problems** that span multiple components or teams. - **Set code-quality and engineering standards for your area,** and lead by example in code review. - **Own the MLOps design for a significant system:** CI/CD, orchestration (Kubernetes/Prefect), and production monitoring. - **Lead root-cause analysis for complex production incidents** and drive systemic fixes, not just patches. - **Extend the team's MLOps frameworks** to support new model types or deployment patterns. - **Lead design of agentic AI systems (e.g. LangGraph-based),** including multi-step reasoning and RAG architecture decisions. - **Own LLMOps practices for your area:** evaluation pipelines, guardrails, and cost/latency optimization. - **Evaluate and introduce new GenAI tools, frameworks, or techniques** where they meaningfully improve the platform. - Mentor other engineers and help raise technical standards within your workstream. ## **Key Tools & Technologies** - **Cloud & Data Infra:** AWS (EC2/ECS, S3, Lambda, IAM, CloudWatch or equivalent); Databricks & Unity Catalog - **Foundational:** Python; Git/GitHub; software engineering best practices; testing; SQL - **MLOps & Deployment:** Docker, Kubernetes, Prefect; CI/CD (GitHub Actions); production monitoring; model versioning & lineage; GitOps governance - **GenAI & Agentic Architecture:** Claude or comparable LLMs; LangGraph or comparable agent frameworks; RAG architectures; vector databases (e.g. Pinecone); prompt engineering & evaluation ## **Required Qualifications** 8–11 years of hands-on experience, with a track record of owning significant technical workstreams end-to-end. - Strong proficiency in Python and a track record of writing clean, testable, production-quality code. - Demonstrated experience owning CI/CD, containerisation, and orchestration for production ML/AI systems. - Proven experience developing or deploying LLM-based applications, including prompt engineering, RAG, or agentic workflows. - Strong working knowledge of containerisation (Docker), orchestration (Kubernetes), and CI/CD pipelines. - Working knowledge of AWS cloud services and Databricks/Unity Catalog or equivalent enterprise data platforms. - Excellent verbal and written communication skills. - Experience working in Agile/Scrum environments. ## **Education** - Bachelor's or Master's degree in Computer Science, Computer Applications, or a related technical field. Lilly is dedicated to helping individuals with disabilities to actively engage in the workforce, ensuring equal opportunities when vying for positions. If you require accommodation to submit a resume for a position at Lilly, please complete the accommodation request form ([https://careers.lilly.com/us/en/workplace-accommodation](https://careers.lilly.com/us/en/workplace-accommodation)) for further assistance. Please note this is for individuals to request an accommodation as part of the application process and any other correspondence will not receive a response. Lilly does not discriminate on the basis of age, race, color, religion, gender, sexual orientation, gender identity, gender expression, national origin, protected veteran status, disability or any other legally protected status. #WeAreLilly ## Related - [Software Developer Jobs](/jobs/software-developer-jobs/) - [Remote Software Developer Jobs](/jobs/remote-software-developer-jobs/) - [DevOps Engineer Jobs](/jobs/devops-engineer-jobs/) - [Remote DevOps Engineer Jobs](/jobs/remote-devops-engineer-jobs/) - [QA (Quality Assurance) Jobs](/jobs/qa-jobs/) - [Remote QA (Quality Assurance) Jobs](/jobs/remote-qa-jobs/) - [Cybersecurity Jobs](/jobs/cybersecurity-jobs/) - [Remote Cybersecurity Jobs](/jobs/remote-cybersecurity-jobs/) - [Product Manager Jobs](/jobs/product-manager-jobs/) - [Remote Product Manager Jobs](/jobs/remote-product-manager-jobs/) - [Business Analyst Jobs](/jobs/business-analyst-jobs/) - [Web3 Jobs](/jobs/web3-jobs/) ## FAQ ### How do I know if Machine Learning Engineer jobs are a good fit for me? Machine Learning Engineer jobs usually fit people who want to build, test, and improve machine learning systems rather than only analyze results. If your background includes model development, data work, or production software responsibilities, this page is a practical place to review openings in September 2026 and compare how each Machine Learning Engineer listing describes its technical expectations. ### Are Machine Learning Engineer jobs more focused on research or production work? Machine Learning Engineer roles can lean toward either research support or production implementation, but the job title often points to applied model work. Read each Machine Learning Engineer posting closely for clues about whether the role emphasizes experiments, model deployment, data pipelines, or system reliability. The listings on this page can help you separate research-heavy roles from engineering-heavy ones. ### Can I apply to Machine Learning Engineer jobs without the exact same job title on my resume? Yes, if your experience maps closely to Machine Learning Engineer responsibilities. A resume with applied machine learning projects, model evaluation, software engineering work, or data pipeline ownership may still be relevant even if your previous title was different. Focus your application on the parts of your background that connect directly to the Machine Learning Engineer job description. ### What should I check before applying to a Machine Learning Engineer job? Before applying, compare the Machine Learning Engineer posting against your actual experience with model building, data preparation, production systems, and collaboration with technical teams. Look for seniority signals, required tools, and whether the role expects ownership from experimentation through deployment. A careful read can prevent applying to roles that are really research scientist, data engineer, or software engineer positions under a similar title. ### Are Machine Learning Engineer jobs suitable for early-career applicants? Some Machine Learning Engineer jobs may be open to early-career applicants, but the page title alone does not guarantee entry-level fit. Check each listing for wording around junior, associate, internship, or required years of experience. Early-career applicants should pay close attention to whether the role expects independent model deployment, production engineering, or close mentorship from a more senior machine learning team. ### How should I compare multiple Machine Learning Engineer job listings? Compare Machine Learning Engineer listings by the actual work described, not just the title. One role may center on training models, another on deploying models into production, and another on improving data workflows. In 2026, it is worth tracking seniority, workplace format, technical requirements, and ownership level before deciding which Machine Learning Engineer applications deserve the most time. ### Apply to Machine Learning Engineer Jobs Faster Use LiftmyCV to match with Machine Learning Engineer roles, tailor your resume for each opening, and auto-apply to relevant jobs without repeating the same application steps.