26,463 Machine Learning Engineer Jobs (May 2026)
Machine learning engineer roles in May 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.
Principal Machine Learning Engineer
HybridJoin Headspace as a Principal Machine Learning Engineer to lead the development of innovative AI solutions aimed at enhancing mental healthcare. You'll create and deploy advanced language-based machine learning applications, manage scalable AI projects, and collaborate with cross-functional teams, all while inspiring technical growth among peers. With 8+ years of experience in machine learning and a Master's degree or equivalent, your expertise will help shape the future of mental health support. This role offers a competitive salary, equity, and comprehensive benefits in a mission-driven organization.
Posted 4 days ago
Principal Machine Learning Engineer
RemoteJoin A1, a pioneering team developing an AI system that comprehensively understands context and proactively plans actions over time. As a Principal Machine Learning Engineer, you will lead the implementation of production-grade machine learning systems, from training to deployment. You'll architect scalable inference systems and collaborate closely with application engineering teams to seamlessly integrate ML solutions. The role requires a strong background in deep learning and a hands-on approach to deploying large-scale ML models, with a commitment to quality and rapid iteration.
Posted 1 week ago
Principal, Machine Learning Engineer
On-siteLila Sciences is seeking a Principal Machine Learning Engineer to design and scale machine learning infrastructure for advanced biomedical applications. This role entails ownership of systems from training to deployment, collaborating closely with AI scientists to drive innovations in automated scientific discovery. The position requires extensive expertise in ML systems engineering, a strong software engineering background, and a passion for translating research into production capabilities. This is a high-impact role ideal for those at the intersection of machine learning and life sciences.
Posted 1 week ago
Junior Machine Learning Engineer
HybridJoin Entain Australia & New Zealand as a Junior Machine Learning Engineer to help transform the sports and entertainment landscape through data-driven solutions. Focus on building and improving machine learning pipelines, collaborating with cross-functional teams, and driving innovation in gaming and wagering experiences. This role offers a unique opportunity to work at scale and impact millions of customers, all within a supportive and ambitious environment.
Posted 3 weeks ago
Staff Machine Learning Engineer
HybridSuperDial is seeking a Machine Learning Engineer to refine LLMs for healthcare-specific conversations, particularly in revenue cycle management. The role involves improving supervised fine-tuning pipelines and developing models that effectively handle administrative workflows. Ideal candidates have experience with open-source LLMs, Python, and PyTorch, along with a keen interest in integrating model behavior with healthcare workflows. Join a mission-driven team committed to transforming healthcare AI.
Posted today
Staff Machine Learning Engineer
RemoteMaterial Security is seeking a Staff Machine Learning Engineer to join their team of experienced engineers. Your role will focus on building, deploying, and maintaining high-quality models that detect security threats such as phishing and sensitive data breaches. The ideal candidate will have extensive experience in machine learning, a strong understanding of data pipelines, and effective communication skills to collaborate across teams. This position is remote, allowing a flexible work environment while contributing to a diverse and inclusive culture.
Posted 2 days ago
Staff Machine Learning Engineer
RemoteA1 is developing a proactive AI chat app aimed at enhancing users' daily tasks through intelligent conversations and workflows. As the Technical Lead in Machine Learning, you will manage the execution layer of A1’s intelligence, focusing on translating research into scalable ML systems. Your responsibilities include overseeing data pipelines, training workflows, and production deployments. A collaborative approach with application engineering is essential, and you will work under real-world constraints to ensure efficiency and performance in production settings.
Posted 1 week ago
Staff Machine Learning Engineer
RemoteDragos, a leader in ICS/OT Cybersecurity, seeks a Staff Machine Learning Engineer. The role focuses on developing and implementing machine learning systems for enhancing threat detection and security analysis in industrial environments. The candidate will collaborate with a team of data professionals and contribute to the AI/ML capabilities of the Dragos platform, fostering a culture of authenticity, transparency, and trust. This remote position offers a competitive salary and comprehensive benefits, making it a prime opportunity for those passionate about cybersecurity and technology.
Posted 1 week ago
Staff Machine Learning Engineer
On-siteAxiado, an AI-enhanced security processor company, is looking for a Staff Machine Learning Engineer to design and implement a comprehensive ML ecosystem for edge devices. This role combines MLOps, edge AI, and system hardware, requiring extensive knowledge in machine learning, programming (Python), and hardware integration. Responsibilities include leading ML architecture, developing autonomous agents, and ensuring security protocols. With a compensation range of $80,000 - $200,000, Axiado values collaboration, respect, and innovation, aiming to solve real-world problems in the tech landscape.
Posted 1 week ago
Staff Machine Learning Engineer
HybridAccurx is transforming healthcare communication with a single platform uniting GPs and patients. They are seeking a Staff Machine Learning Engineer to drive AI/ML capabilities across teams and address healthcare challenges. The ideal candidate will lead technical direction, establish quality strategies, mentor engineers, and identify impactful AI/ML opportunities while contributing to a mission-driven environment that values innovative approaches in healthcare.
Posted 2 weeks ago
Staff Machine Learning Engineer
HybridAuror is seeking a Staff Machine Learning Engineer to enhance operational capabilities in the retail sector. The role focuses on leveraging machine learning to derive insights from extensive datasets, improving crime detection, and reporting in partnership with leading retailers. This position requires collaboration across teams to design and implement machine learning solutions that operate effectively in real-world scenarios, emphasizing responsible AI practices.
Posted 2 weeks ago
Staff Machine Learning Engineer
HybridBumble Inc. is searching for a Staff Machine Learning Engineer to join their team in Austin, Texas. The ideal candidate will play a key role in developing and optimizing machine learning systems that enhance user experiences across Bumble's platforms. This position requires significant technical expertise in machine learning, Python, and distributed systems, along with strong collaborative and mentoring skills. The candidate will work in a hybrid setting and is expected to bring deep knowledge and experience to influence technical direction and maintain high standards of excellence.
Posted 2 weeks ago
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Machine Learning Engineer Salary Data (May 2026)
This salary section summarizes pay information from 26,463+ active Machine Learning Engineer postings, including roles focused on model development, production ML systems, and applied machine learning work.
Average Salary
$170k
$201k
$233k
25th
50th
75th
Based on 26,463 roles currently tracked by LiftmyCV. Last updated on May 7, 2026
Salary Distribution
Based on 26,463 roles currently tracked by LiftmyCV. Last updated on May 7, 2026
| Experience Level | 25th Percentile | Median (50th) | 75th Percentile | Sample Size |
|---|---|---|---|---|
| Overall | $169,750 | $201,250 | $232,750 | 7,446 |
| Entry-Level | $78,000 | $101,100 | $118,300 | 4 |
| Mid-Level | $150,000 | $195,000 | $221,950 | 37 |
| Senior-Level | $184,500 | $212,500 | $236,525 | 71 |
"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.