Machine Learning Research Scientist Jobs
Machine Learning Research Scientist jobs are open across technology, healthcare, finance, and defense, covering specializations in deep learning, natural language processing, and reinforcement learning, from new-grad researcher to principal and staff scientist. Scan the live roles below and apply to whichever ones fit.
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Showing 5 of 167+ Machine Learning Research Scientist 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 Scientist Jobs by Experience Level
See All 167+ Machine Learning Research Scientist Jobs
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Find JobsMachine Learning Research Scientist Job Market
Who's Hiring
- Meta24
- Apple19
- SentiLink12
- Scale AI11
- Nuro8
Top Industries Hiring
- Technology & Software29
- Electronics & Hardware12
- Artificial Intelligence11
- Transportation & Logistics7
- Investment & Asset Management5
What Employers Look For
The qualifications that appear most often in machine learning research scientist jobs.
- PhD in machine learning, computer science, statistics, 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
- Strong publication record at venues such as NeurIPS, ICML, ICLR, or CVPR
- Hands-on experience with distributed training and cloud compute infrastructure
- Ability to communicate research findings clearly to both technical and non-technical stakeholders
Tips for Your Machine Learning Research Scientist Job Search
Lead your resume with published work
Machine learning research scientist hiring panels weight publications, preprints, and conference papers heavily. List each paper with the venue name, year, and your authorship position. Reviewers scan this section first and use it to gauge the depth of your independent research.
Match your GitHub to the job description
Before applying, audit your public repositories and pin the ones that use frameworks the job posting names. If a role emphasizes PyTorch and distributed training, your pinned repos should reflect that stack, not a mix of unrelated projects from coursework.
Apply early to roles that fit
Migrate Mate lists machine learning research scientist openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Target postings that name your research area
Filter openings by the subfield you work in, such as computer vision, generative models, or time-series forecasting. Applying to roles that name your exact research area increases the chance your resume reaches a hiring manager who can evaluate it rather than a recruiter who cannot.
Prepare a research talk for the interview loop
Most research scientist loops include a seminar-style presentation on your past work. Prepare a 30-minute talk that covers your problem formulation, experimental design, and ablation results. Interviewers will ask hard follow-up questions, so know the limitations of your own work cold.
Negotiate start date around publication deadlines
If you have a paper under review or a submission deadline approaching, raise it during offer negotiation. Many research teams will adjust start dates to let you complete pending submissions, and asking early signals that you treat your research output seriously.
Machine Learning Research Scientist Jobs: Frequently Asked Questions
Which companies are hiring the most machine learning research scientists?
Meta, Apple, and SentiLink are hiring the most machine learning research scientists right now, with openings concentrated in California, Washington, and New York, based on current listings on Migrate Mate as of November 2026. Research-focused roles are also active at national labs, defense contractors, and large healthcare systems.
How many machine learning research scientist jobs are remote?
About 55% of machine learning research scientist openings are fully remote or hybrid as of November 2026, with remote availability highest in natural language processing and generative AI research roles. Positions that require access to on-premises compute clusters or specialized hardware, such as robotics and embedded ML research, are more likely to require on-site presence.
How do you become a machine learning research scientist?
Most machine learning research scientists complete a PhD in a quantitative field, publishing original work during their doctoral program. Building a track of peer-reviewed publications or strong preprints is the clearest signal to employers. Some researchers enter from industry after completing a master's degree paired with open-source contributions and demonstrated research output. Internships at research labs during graduate school also significantly strengthen your candidacy.
How do you get hired as a machine learning research scientist with little experience?
Start by contributing to open-source ML projects and posting preprints on arXiv to establish a visible research record before you have formal publications. Research assistant and associate researcher roles at universities or labs are designed as entry points and explicitly do not require prior industry experience. Focus your application on roles that list a master's degree as sufficient and emphasize any independent projects, competition results, or workshop papers you have produced.
What does the machine learning research scientist interview process look like?
The process typically includes an initial technical screen covering ML fundamentals and coding, followed by a research presentation where you walk through a past project in depth. After that comes a full interview loop with individual sessions on research taste, system design for ML applications, and collaborative problem-solving. Some teams also include a written take-home assignment asking you to review a paper or propose an experiment.
Where can I find and apply to machine learning research scientist jobs?
You can find and apply to machine learning research scientist jobs on Migrate Mate, which lists current openings from across the United States. Search for roles that match your research area and experience level, then apply directly to each listing that fits.
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Find roles that match your experience and apply in just a few clicks.
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