Machine Learning Research Engineer Jobs
Machine Learning Research Engineer jobs are open across technology, healthcare, finance, and defense, from new-grad to principal and staff levels, with specializations in deep learning, natural language processing, and reinforcement learning. Find a role that fits below and apply directly.
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Showing 5 of 202+ Machine Learning Research Engineer jobs
The Position
A healthier future. It’s what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That’s what makes us Roche.
Advances in AI, data, and computational sciences are transforming drug discovery and development. Roche’s Research and Early Development organisations at Genentech (gRED) and Pharma (pRED) have demonstrated how these technologies accelerate R&D, leveraging data and novel computational models to drive impact. Seamless data sharing and access to models across gRED and pRED are essential to maximising these opportunities. The new Computational Sciences Center of Excellence (CoE) is a strategic, unified group whose goal is to harness the transformative power of data and Artificial Intelligence (AI) to assist our scientists in both pRED and gRED to deliver more innovative and transformative medicines for patients worldwide.
The Opportunity
Within AI for Drug Discovery, the Frontier Research team advances fundamental machine learning and statistics methods, motivated by real-world challenges in drug discovery. In biology and medicine, many important problems cannot yet be addressed with off-the-shelf machine learning approaches—they demand not only novel solutions, but also new paradigms for framing and thinking about the problems themselves. Instead of focusing on incremental improvements, we work on long-horizon ideas that require a creative and broad perspective, developing solutions at a level that can be instantiated for multiple internal and external applications. If you’re excited about this kind of research, join us on an impactful journey of innovation at Prescient.
In this role, you will:
- You will develop new methods and frameworks for uncertainty quantification, statistical inference, and experimental design, motivated by the diverse problem settings that arise when using machine learning to guide decision-making across the drug-discovery pipeline.
- You are expected to contribute to and drive research projects that result in conference and/or journal publications and open-source code, and that foster cross-organizational collaborations and applications.
- You are expected to engage actively in knowledge dissemination, presenting your work & expertise at internal and external scientific conferences.
- You will collaborate closely with our team in South San Francisco, New York, and Basel, Switzerland.
Who You Are
- Must be pursuing, or have completed within the last 12 months, a Ph.D. in Computer Science, Statistics, Applied Mathematics, Physics, Computational Biology, or related technical field, or have demonstrated equivalent research productivity.
- Demonstrated expertise in (any paradigm of) uncertainty quantification for decision-making, e.g., sequential/online statistical inference, experimental design, conformal prediction, reinforcement learning, calibration, game-theoretic statistics, etc.
- Proven publication record and experience contributing to the research community, including at conferences like NeurIPS, ICML, ICLR, AISTATS, UAI, etc., and/or relevant journals.
- Excellent skills in technical writing & communication, as well as collaboration and interpersonal skills.
Preferred
- Experience with developing machine learning and/or statistical methods for biological/clinical data is desired, but not necessary.
- Experience developing open-source packages preferred.
Relocation benefits are NOT available for this job posting
The expected salary range for this position, based on the primary location of California, is $147,600 - 274,000. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. A discretionary annual bonus may be available based on individual and Company performance. This position also qualifies for the benefits detailed at the link provided below.
Benefits
#ComputationCoE
#tech4lifeComputationalScience
#tech4lifeAI
Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.
If you have a disability and need an accommodation in relation to the online application process, please contact us by completing this form Accommodations for Applicants.
The Position
A healthier future. It’s what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That’s what makes us Roche.
Advances in AI, data, and computational sciences are transforming drug discovery and development. Roche’s Research and Early Development organisations at Genentech (gRED) and Pharma (pRED) have demonstrated how these technologies accelerate R&D, leveraging data and novel computational models to drive impact. Seamless data sharing and access to models across gRED and pRED are essential to maximising these opportunities. The new Computational Sciences Center of Excellence (CoE) is a strategic, unified group whose goal is to harness the transformative power of data and Artificial Intelligence (AI) to assist our scientists in both pRED and gRED to deliver more innovative and transformative medicines for patients worldwide.
The Opportunity
Within AI for Drug Discovery, the Frontier Research team advances fundamental machine learning and statistics methods, motivated by real-world challenges in drug discovery. In biology and medicine, many important problems cannot yet be addressed with off-the-shelf machine learning approaches—they demand not only novel solutions, but also new paradigms for framing and thinking about the problems themselves. Instead of focusing on incremental improvements, we work on long-horizon ideas that require a creative and broad perspective, developing solutions at a level that can be instantiated for multiple internal and external applications. If you’re excited about this kind of research, join us on an impactful journey of innovation at Prescient.
In this role, you will:
- You will develop new methods and frameworks for uncertainty quantification, statistical inference, and experimental design, motivated by the diverse problem settings that arise when using machine learning to guide decision-making across the drug-discovery pipeline.
- You are expected to contribute to and drive research projects that result in conference and/or journal publications and open-source code, and that foster cross-organizational collaborations and applications.
- You are expected to engage actively in knowledge dissemination, presenting your work & expertise at internal and external scientific conferences.
- You will collaborate closely with our team in South San Francisco, New York, and Basel, Switzerland.
Who You Are
- Must be pursuing, or have completed within the last 12 months, a Ph.D. in Computer Science, Statistics, Applied Mathematics, Physics, Computational Biology, or related technical field, or have demonstrated equivalent research productivity.
- Demonstrated expertise in (any paradigm of) uncertainty quantification for decision-making, e.g., sequential/online statistical inference, experimental design, conformal prediction, reinforcement learning, calibration, game-theoretic statistics, etc.
- Proven publication record and experience contributing to the research community, including at conferences like NeurIPS, ICML, ICLR, AISTATS, UAI, etc., and/or relevant journals.
- Excellent skills in technical writing & communication, as well as collaboration and interpersonal skills.
Preferred
- Experience with developing machine learning and/or statistical methods for biological/clinical data is desired, but not necessary.
- Experience developing open-source packages preferred.
Relocation benefits are NOT available for this job posting
The expected salary range for this position, based on the primary location of California, is $147,600 - 274,000. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. A discretionary annual bonus may be available based on individual and Company performance. This position also qualifies for the benefits detailed at the link provided below.
Benefits
#ComputationCoE
#tech4lifeComputationalScience
#tech4lifeAI
Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.
If you have a disability and need an accommodation in relation to the online application process, please contact us by completing this form Accommodations for Applicants.
Machine Learning Research Engineer Jobs by Experience Level
See All 202+ Machine Learning Research Engineer Jobs
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Find JobsMachine Learning Research Engineer Job Market
Who's Hiring
- Apple36
- Meta24
- Scale AI18
- Nuro8
- Microsoft7
Top Industries Hiring
- Technology & Software24
- Artificial Intelligence18
- Electronics & Hardware13
- Transportation & Logistics7
- Investment & Asset Management6
What Employers Look For
The qualifications that appear most often in machine learning research engineer jobs.
- PhD or Master's degree in machine learning, computer science, or a closely related field
- Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow
- Experience designing and running large-scale machine learning experiments on distributed systems
- Peer-reviewed publications or preprints in relevant machine learning conferences or journals
- Hands-on experience with model training, evaluation, and deployment at production scale
- Familiarity with cloud computing platforms and GPU cluster management for research workloads
Tips for Your Machine Learning Research Engineer Job Search
Tailor your resume to publication depth
Hiring committees for research roles weight peer-reviewed publications and arXiv preprints heavily. List each paper with the venue name, your authorship position, and citation count if notable. Generic project bullets won't distinguish you from applied engineers.
Show benchmark results, not just methods
Describe your research contributions with concrete benchmark outcomes on standard datasets. Saying you improved model accuracy on a named benchmark by a specific margin tells reviewers far more than describing the architecture you used.
Apply early to roles that fit
Migrate Mate lists machine learning research engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Target postings by research area explicitly
Filter openings by the research subdomain closest to your work, such as computer vision, generative models, or graph neural networks. Applying to roles aligned with your publication record dramatically increases callback rates compared to broad applications.
Prepare a research talk, not just a whiteboard session
Many research engineering interviews include a 30-to-45-minute presentation on a past project. Practice framing your problem statement, methodology, and failure modes for a mixed audience of engineers and scientists who weren't in your lab.
Negotiate compute and research time, not only pay
Research roles vary widely on how much independent research time and GPU cluster access you receive. Ask explicitly about publication expectations, internal compute allocation, and whether you can contribute to open-source projects before you sign.
Machine Learning Research Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most machine learning research engineers?
The companies hiring the most machine learning research engineers right now include Apple, Meta, and Scale AI, with the largest share of openings in California, Washington, and New York, based on current listings on Migrate Mate as of November 2026. Research-focused roles tend to cluster at organizations with dedicated AI labs or large-scale product surfaces that benefit from novel model development.
How many machine learning research engineer jobs are remote?
About 40% of machine learning research engineer openings are fully remote or hybrid as of November 2026, reflecting the compute-heavy and collaborative nature of the role. Sub-areas like natural language processing and generative modeling tend to have the highest remote share, while robotics and hardware-adjacent research roles are more likely to require on-site presence.
How do you become a machine learning research engineer?
Most machine learning research engineers start with a strong foundation in linear algebra, probability, and algorithms, then develop applied skills through graduate research, internships, or open-source contributions. Building a public portfolio of reproducible experiments, contributing to research codebases, and publishing or presenting work at workshops or conferences are the most direct paths to landing a research engineering role.
Can you get a machine learning research engineer job with little experience?
Entry-level machine learning research engineer roles exist but are competitive. Candidates with limited industry experience strengthen their case by publishing undergraduate or independent research, contributing to open-source machine learning libraries, winning or placing well in public competitions, or completing a research internship where they can demonstrate end-to-end project ownership and experimental rigor.
What does the machine learning research engineer interview process look like?
The interview process typically includes a recruiter screen, a technical phone round covering coding and machine learning fundamentals, a research presentation where you walk through a past project in depth, and a full-day virtual or on-site loop with coding, system design, and research discussion panels. Some organizations also include a take-home research problem or a live experiment design session with the team.
Where can I find and apply to machine learning research engineer jobs?
You can find and apply to machine learning research engineer jobs on Migrate Mate, which lists current openings from across the United States in one place. Find roles that match your research background and seniority level, then apply directly to each listing that fits.
See All 202+ Machine Learning Research Engineer Jobs
Find roles that match your experience and apply in just a few clicks.
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