4,379 Machine Learning Engineer Jobs (November 2026)
Machine learning engineer roles in November 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.
Applied Machine Learning Engineer
On-siteJoin the Aerospace Corporation as an Applied Machine Learning Engineer to contribute to critical space programs. This full-time role involves translating AI research into scalable solutions, working collaboratively with engineers and data…
Posted 3 weeks ago
Applied Machine Learning Engineer
HybridPermitFlow is redefining how America builds. We’re an applied AI company serving the nation’s builders, tackling one of the largest information challenges in the economy: understanding what can be built, where, and how . Our AI agent workforce…
Posted 4 weeks ago
Lead Machine Learning Engineer
On-siteWe are UMG, the Universal Music Group. We are the world’s leading music company. In everything we do, we are committed to artistry, innovation and entrepreneurship. We own and operate a broad array of businesses engaged in recorded music,…
Posted 3 days ago
Lead Machine Learning Engineer
On-siteDT (NASDAQ: APPS) is unifying the full mobile experience. Our growth platform powers the mobile ecosystem by bringing together advertisers, app developers, and device partners, to create a unique and premium value exchange, that results in a…
Posted 2 weeks ago
Lead Machine Learning Engineer
On-siteSalesforce is seeking a Lead Machine Learning Engineer to define and execute a technical ML strategy within the Trust Intelligence Platform organization. The role involves developing and optimizing machine learning pipelines, focusing on…
Posted 3 weeks ago
Lead Machine Learning Engineer
ANYHalter is seeking a Lead Machine Learning Engineer to drive the development of predictive systems that enhance agricultural operations. The role requires expertise in machine learning and complex systems to build advanced models for improving…
Posted 3 weeks ago
Senior Applied Machine Learning Engineer
On-siteSenior Applied Machine Learning Engineer This role has been designed as 'Hybrid' with a requirement that you will work on average 2 days per week from an HPE office. Who We Are: Hewlett Packard Enterprise is the global edge-to-cloud company…
Posted 2 weeks ago
Sr. Applied Machine Learning Engineer
ANYJob Title: Sr. Applied Machine Learning Engineer Team: Engineering Location: Onsite - Sawtelle (Between Santa Monica West LA) Employment Type: Full-time, Salary, Exempt Reports To: Head of Engineering ABOUT PLUG Plug is an automotive fintech…
Posted 4 days ago
Machine Learning Engineer/Senior Machine Learning Engineer
On-siteRoche is seeking a motivated ML Infrastructure DevOps Engineer to join its AI for Drug Discovery group. The role focuses on building and maintaining scalable ML infrastructure on AWS, including automating deployment, managing CI/CD pipelines,…
Posted 2 weeks ago
Principal Machine Learning Engineer
On-siteAbout us Re-architecting Engineering for the Age of Intelligence PhysicsX is the physics AI company for industrials. The company’s mission is to accelerate hardware innovation by overhauling what industrial engineering and manufacturing…
Posted 4 days ago
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Principal Machine Learning Engineer
On-siteZscaler is seeking a Principal Machine Learning Engineer to lead the development of advanced machine learning models within a hybrid role based in Bangalore. The selected candidate will mentor junior members, drive strategic AI and data…
Posted 5 days ago
Principal Machine Learning Engineer
On-siteThe application window is expected to close on: 11/19/2026 About the Team The AI Software Platform Team designs and builds enterprise AI platform software to enhance security resilience, threat defense, and operational automation across…
Posted 2 weeks ago
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Machine Learning Engineer Salary Data (November 2026)
This salary section summarizes pay information from 4,379+ active Machine Learning Engineer postings, including roles focused on model development, production ML systems, and applied machine learning work.
Average Salary
$165k
$200k
$250k
25th
50th
75th
Based on 4,379 roles currently tracked by LiftmyCV. Last updated on Oct 19, 2026
Salary Distribution
Based on 4,379 roles currently tracked by LiftmyCV. Last updated on Oct 19, 2026
| Experience Level | 25th Percentile | Median (50th) | 75th Percentile | Sample Size |
|---|---|---|---|---|
| Overall | $165,000 | $200,000 | $250,000 | 4,379 |
| Entry-Level | $57,862.5 | $99,625 | $137,687.5 | 8 |
| Mid-Level | $149,491.88 | $189,500 | $242,500 | 88 |
| Senior-Level | $181,562.5 | $208,875 | $251,550 | 152 |
"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.