18,038 Machine Learning Engineer Jobs (June 2026)
Machine learning engineer roles in June 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.
Junior Machine Learning Engineer
On-siteProvidius, a leader in Media & Entertainment innovation for over a decade, is seeking a Junior Machine Learning Engineer to grow within the organization. This role emphasizes hands-on experience with real data, supporting the development of machine learning models and data pipelines. Working alongside senior engineers, you will implement models, prepare datasets, and contribute to experiments while building a strong foundation in applied machine learning.
Posted 6 days ago
Principal Machine Learning Engineer
ANYIbotta is looking for a Principal Machine Learning Engineer to enhance its Core Data & Analytics team. This role entails leading complex ML initiatives and defining ML strategy while mentoring other engineers and data scientists. Candidates will utilize their expertise in scalable machine learning solutions, primarily using technologies like Python and AWS. Positions are available in Denver with the option for remote work in multiple states. The ideal candidate will have over 10 years of relevant experience and a strong background in ML frameworks and cloud infrastructure.
Posted 1 week ago
Principal Machine Learning Engineer
RemoteArmis is looking for a Principal Machine Learning Engineer to join their Strategic Initiatives team. The role involves building and optimizing AI pipelines for data processing and model training while collaborating with data scientists and engineers. Candidates should have a strong background in AI/ML pipelines, proficiency in Python, and familiarity with cloud platforms. The position offers a competitive salary and emphasizes work-life balance in an inclusive workplace.
Posted 2 weeks ago
Principal Machine Learning Engineer
HybridA1 is looking for a Principal Machine Learning Engineer to lead the development of production-grade ML systems for a proactive AI smart assistant. The role involves building end-to-end ML pipelines, optimizing performance, and collaborating with application engineering teams. Candidates should have a deep understanding of deep learning, experience in training and deploying large-scale models, and strong software engineering skills. The position is poised to drive innovative AI solutions, with a focus on reliability and efficiency, in a fast-paced and hands-on environment.
Posted 2 weeks ago
.png?1713343411){kind=small}
Principal Machine Learning Engineer
ANYThe Principal Machine Learning Engineer role at Doctolib involves designing and implementing advanced machine learning models to enhance healthcare solutions. Candidates should have a strong background in data analysis, machine learning algorithms, and software engineering practices. The position focuses on building innovative AI-driven tools, collaborating closely with cross-functional teams to ensure the successful application of machine learning in healthcare contexts.
Posted 3 weeks ago
Staff Machine Learning Engineer
On-siteMariana Minerals is seeking a Staff Machine Learning Engineer to build an autonomous critical minerals supply chain, focusing on refining processes. This role combines advanced software and data-driven decision making to produce battery-grade lithium salts from unconventional sources. As a key player, you will set the technical direction for refining autonomy, solve complex modeling challenges, and collaborate with leadership on strategic decisions. This position emphasizes innovation in the mining industry, promising a transformative impact on critical mineral production for the future.
Posted 1 week ago
Staff Machine Learning Engineer
HybridA1 is seeking a Staff Machine Learning Engineer to lead the execution of their proactive AI smart assistant. This role involves translating research into scalable ML systems that handle complex real-world tasks. As the Technical Lead, you will oversee data pipelines, model deployment, and collaboration with engineering teams to ensure effective integration and performance. Ideal candidates have experience in building production-grade ML systems and strong coding skills. This position offers the chance to contribute to a high-impact project in a dynamic work environment.
Posted 2 weeks ago
Staff Machine Learning Engineer
On-siteLucid Motors is redefining the luxury electric vehicle experience with advanced technology and design. We are seeking a Staff Software Engineer to focus on integrating and deploying perception models for our ADAS and autonomous driving systems. This role will involve collaboration with ML researchers and engineers to deliver high-performance solutions, optimize performance, and develop automated pipelines. Candidates should have a strong background in software engineering and proficiency in C++ and Python, with significant experience in perception systems.
Posted 3 weeks ago
Lead Machine Learning Engineer
On-siteAs a Lead Machine Learning Engineer at Salesforce, you will be pivotal in shaping our cybersecurity defense strategy through innovative machine learning techniques. You will mentor junior team members and operationalize data-driven strategies to enhance threat detection capabilities. This role calls for an expert with extensive experience in data science and a strong background in cybersecurity, leading to improved organizational resilience against sophisticated threats. You will work with cutting-edge technology, creating impactful solutions in the rapidly evolving landscape of AI-driven customer success.
Posted 6 days ago
Lead, Machine Learning Engineer
On-siteJoin Prudential's Global Technology team as a Lead, Machine Learning Engineer, where you'll implement machine learning models to solve complex business problems and optimize operations. Collaborate with diverse teams and leverage your technical expertise to drive innovation in a leading financial services institution. You'll enjoy a culture focused on continuous learning and inclusive leadership, with a commitment to employee development and well-being.
Posted 1 week ago
Lead Machine Learning Engineer
On-siteCohere Health is looking for a Lead Machine Learning Engineer to join their team. The role involves designing and deploying machine learning models to automate clinical practices. Candidates should have substantial experience in applied machine learning, especially in healthcare or technology settings. Strong understanding of model building and familiarity with Python and deep learning frameworks are essential. The position requires cross-functional collaboration with various stakeholders to align ML solutions with organizational goals.
Posted 1 week ago
Lead Machine Learning Engineer
HybridJoin Faculty as a Lead Machine Learning Engineer, where you'll spearhead innovative AI projects using your deep technical expertise and leadership skills. Collaborate with a dedicated team in retail and consumer sectors to drive impactful solutions leveraging advanced technologies. In this role, you'll manage high-risk projects and shape the future of AI applications, ensuring scalable and reliable systems. Faculty values diversity and actively encourages all qualified individuals to apply.
Posted 2 weeks ago
How LiftmyCV Helps with Machine Learning Engineer Jobs Search
LiftmyCV combines AI matching, resume generation, and auto-apply to streamline every step of your machine learning engineer jobs job search—from discovery to interview.
Discover Machine Learning Engineer Jobs Across 18,038+ Openings
AI scans millions of listings across 10+ job boards and surfaces the most relevant roles for your profile.
Learn more →Create Job-Specific Materials for Machine Learning Engineer Jobs Roles
Auto-generates tailored resumes and cover letters matched to each job description and ATS requirements.
Learn more →Auto-Apply and Automate Machine Learning Engineer Jobs Applications
Set your preferences and let the AI agent apply to matching jobs automatically, with full tracking and control.
Learn more →
Machine Learning Engineer Salary Data (June 2026)
This salary section summarizes pay information from 18,038+ active Machine Learning Engineer postings, including roles focused on model development, production ML systems, and applied machine learning work.
Average Salary
$150k
$184k
$226k
25th
50th
75th
Based on 18,038 roles currently tracked by LiftmyCV. Last updated on Jun 6, 2026
Salary Distribution
Based on 18,038 roles currently tracked by LiftmyCV. Last updated on Jun 6, 2026
| Experience Level | 25th Percentile | Median (50th) | 75th Percentile | Sample Size |
|---|---|---|---|---|
| Overall | $149,500 | $184,452 | $226,250 | 6,566 |
| Entry-Level | $87,920 | $145,000 | $145,650 | 5 |
| Mid-Level | $151,250 | $175,000 | $222,212.5 | 26 |
| Senior-Level | $167,500 | $205,000 | $242,988.5 | 41 |
"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.
Required Skills
Resume Tips
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.
How to Prepare for Interviews
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.