ML Engineer Jobs
ML Engineer jobs are open across technology, healthcare, financial services, and autonomous systems, from new-grad to staff and principal levels, with specializations in NLP, computer vision, and MLOps. Find a role that fits from the openings below and apply directly.
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A growing AI and Data Science team at a healthcare-focused company is looking for a Senior Machine Learning Engineer to take ownership of complex, enterprise-scale ML initiatives. This is a W2 contract role for work-authorized candidates (no visa sponsorship available). You'll work in a fast-paced environment building production-grade ML solutions that directly impact patient outcomes and healthcare operations — with a strong emphasis on compliance, reliability, and end-to-end ownership.
Ideal candidates bring 8+ years of professional ML engineering experience and a mandatory background in the healthcare industry, including hands-on experience with HIPAA-compliant systems and sensitive patient data.
What You'll Do
Own the full ML lifecycle: data ingestion, feature engineering, model training, evaluation, deployment, monitoring, retraining, and maintenance.
Design and build scalable, production-ready ML systems with high availability, performance, and reliability.
Develop and maintain MLOps pipelines — including CI/CD, model registry, feature stores, automated deployment, monitoring, and rollback strategies.
Monitor production models for drift (model, data, accuracy degradation) and overall system health.
Build and integrate REST APIs to connect ML services into enterprise cloud applications.
Optimize models for latency, scalability, reliability, and operational cost.
Provide technical leadership on AI/ML initiatives across the organization.
Collaborate with Data Engineers, Software Engineers, Product Managers, Clinical teams, and business stakeholders.
Ensure strict compliance with HIPAA, PHI, PII, and enterprise security standards throughout all ML workflows.
What We're Looking For
Required — Dealbreakers:
8+ years of professional software engineering and machine learning experience.
Healthcare domain experience is mandatory — including HIPAA compliance and handling of sensitive patient data (PHI/PII).
Demonstrated ownership of end-t...
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ML Engineer Jobs by Experience Level
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Who's Hiring
- Apple192
- Google50
- Meta48
- General Motors48
- JPMorganChase46
Top Industries Hiring
- Technology & Software288
- Electronics & Hardware165
- Banking & Financial Services88
- Automotive65
- Consulting & Professional Services40
What Employers Look For
The qualifications that appear most often in ML engineer jobs.
- Proficiency in Python and ML frameworks such as PyTorch or TensorFlow
- Experience building and deploying models in cloud environments like AWS, GCP, or Azure
- Familiarity with MLOps tools and practices including CI/CD pipelines for model deployment
- Strong foundation in statistics, linear algebra, and machine learning fundamentals
- Experience with data processing tools such as Spark, SQL, or distributed computing platforms
- Bachelor's or master's degree in computer science, statistics, or a related quantitative field
Tips for Your ML Engineer Job Search
Quantify model impact on your resume
Recruiters scan for outcomes, not just tools. Replace 'built a recommendation model' with metrics like latency reduction, precision gains, or revenue lift. If you can't share exact figures due to confidentiality, describe the scale of the dataset or the business problem solved.
Tailor your GitHub to the posting
Before you apply, pin the repos most relevant to that job's stack. An NLP-focused team cares about your transformer experiments, not your random forest notebooks. A targeted profile signals genuine fit faster than a resume bullet ever will.
Apply early to roles that fit
Migrate Mate lists ml engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Decode the job level before applying
ML engineer titles vary wildly across companies. A 'senior' at a startup may expect you to own infra end-to-end, while the same title at a large tech firm may mean pure modeling work. Read the responsibilities section for scope signals, not just the title.
Prepare a system design answer for ML
Most mid-level and senior interviews include an ML system design round covering feature pipelines, serving infrastructure, and monitoring. Practice walking through a real-time inference system out loud before your first interview, not the night before your third.
Negotiate with competing offers in hand
ML compensation packages often include equity, signing bonuses, and compute credits that are more negotiable than base pay. If you're in multiple processes, time your final rounds to land offers close together so you can negotiate from a position of genuine choice.
ML Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most ml engineers?
The companies hiring the most ml engineers right now include Apple, Google, and Meta, with the largest share of openings in California, Washington, and New York, based on current listings on Migrate Mate as of August 2026. Demand is concentrated in technology, financial services, and healthcare, though openings appear across a broad range of industries.
How many ml engineer jobs are remote?
About 56% of ml engineer openings are fully remote or hybrid as of August 2026, making it one of the more remote-accessible engineering roles. Research and experimentation-heavy positions tend to offer the most location flexibility, while roles with heavy data infrastructure or on-premise compute requirements are more likely to require in-person work.
How do you become a ml engineer?
You typically start by building a strong foundation in Python, linear algebra, and core ML concepts through coursework or self-study, then reinforce that with hands-on projects covering supervised learning, model evaluation, and deployment. From there, gaining experience with cloud platforms and MLOps tooling, contributing to open-source projects, and building a GitHub portfolio that demonstrates end-to-end model work will make your application competitive for entry-level roles.
How do you get hired as a ml engineer with little experience?
Focus on building a portfolio of end-to-end projects that go beyond training a model to include data preprocessing, evaluation, and a deployed endpoint or API. Kaggle competitions, research assistantships, and internships in data engineering or analytics are common entry points. Applying to roles titled 'junior ml engineer' or 'machine learning associate' gives you a more realistic starting point than aiming directly at senior positions.
What does the ml engineer interview process look like?
Most ml engineer interview processes include a recruiter screen, a technical phone screen covering Python and ML fundamentals, a take-home or live coding round focused on data manipulation and model building, and a final loop with an ML system design round and behavioral interviews. Senior-level processes often include a research presentation or a deep dive into a past project, where interviewers probe your decision-making and trade-offs as much as your technical output.
Where can I find and apply to ml engineer jobs?
You can find and apply to ml engineer jobs on Migrate Mate, which lists current openings from across the United States. Find roles that match your skills and target level, then apply directly to each listing. Migrate Mate aggregates openings in one place so you're not jumping between employer career pages to track down active postings.
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