Machine Learning Research Jobs in Massachusetts
Machine Learning Research jobs in Massachusetts are highly active, concentrated in the Boston metro's biotechnology, robotics, and defense technology sectors, with openings at every level from research associate through principal scientist. Cambridge, Boston, and Waltham are the top hiring centers, anchored by institutions and companies like MIT Lincoln Laboratory, Raytheon Technologies, and Biogen, all of which maintain deep, ongoing research teams in the state. The most in-demand specialties are natural language processing, computer vision, and reinforcement learning for healthcare and life sciences applications. Find a role that fits below and apply directly.
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Showing 5 of 9+ Machine Learning Research jobs
At Bayer we’re visionaries, driven to solve the world’s toughest challenges and striving for a world where 'Health for all Hunger for none’ is no longer a dream, but a real possibility. We’re doing it with energy, curiosity and sheer dedication, always learning from unique perspectives of those around us, expanding our thinking, growing our capabilities and redefining ‘impossible’. There are so many reasons to join us. If you’re hungry to build a varied and meaningful career in a community of brilliant and diverse minds to make a real difference, there’s only one choice.
Head of Machine Learning Research
We are seeking a scientific and technical leader to advance machine learning–enabled molecular design across our drug discovery portfolio. As a member of the Data Science & AI leadership team, you will define and deliver the models, platforms, and ways of working that shorten design–make–test–analyze cycles and improve the quality of molecules progressing toward nomination.
- Co-own the vision, roadmap, and investment case for ML-driven molecule design and multi-parameter optimization across small molecules and emerging modalities.
- Make build/buy/partner decisions across generative design, property prediction, structure-based ML, and synthesis planning; evaluate foundation models and external platforms with rigor rather than hype.
- Set the standard for how models are validated, benchmarked prospectively, and retired — including uncertainty quantification, applicability domain, and honest reporting of failure modes.
- Partner with Drug Discovery Sciences — medicinal chemistry, structural biology, biophysics, screening, DMPK, and safety — to embed ML into live programs from hit identification through candidate nomination.
- Co-own program-level design goals with chemistry leads: potency, selectivity, ADMET, developability, and IP position optimized together rather than sequentially.
- Drive active learning and closed-loop DMTA cycles, including integration with high-throughput and automated synthesis where available.
- Ensure models are delivered as reliable, supported products that chemists actually use, not one-off analyses.
- Direct development of core capabilities: ADMET and property predictors, generative and de novo design, retrosynthesis and synthetic accessibility, and structure-based ML including co-folding, pose prediction, docking rescoring, and ML-accelerated free energy methods.
- Work with data engineering to secure the assay, structural, and DMTA data foundation these models depend on — curation, provenance, harmonization across assays and sites, and feedback capture from every make-test cycle.
- Establish MLOps practice appropriate to a regulated R&D environment: versioning, reproducibility, monitoring, and documentation.
- Build, lead, and develop a team of approximately 25 spanning ML research, computational chemistry, and ML engineering; grow leaders within the group.
- Own budget, vendor relationships, and external collaborations with academic groups, consortia, and biotech partners.
- Represent the function to R&D leadership, translating technical capability into portfolio impact and translating portfolio priorities into technical strategy.
- Contribute to the broader scientific community through publication, presentation, and precompetitive collaboration where appropriate.
- PhD in computational chemistry, cheminformatics, structural biology, biophysics, computer science, or a related field, with demonstrated depth in both a molecular science and machine learning. Exceptional candidates with an MSc and equivalent depth of experience will be considered;
- Deep, current expertise across modern ML for molecules: graph neural networks, transformers, generative approaches (diffusion, flow matching, autoregressive), Bayesian optimization and active learning, transfer learning on sparse assay data, and uncertainty quantification;
- Working command of the discovery domain: SAR interpretation, multi-parameter optimization, ADMET and developability, protein structure and ligand binding, docking and free energy methods;
- Demonstrated impact on real programs — molecules advanced, cycles shortened, decisions changed. A publication record without program impact is not sufficient for this role;
- Proven ability to partner with medicinal chemists and structural biologists as scientific peers, including the credibility to challenge and be challenged on design decisions;
- Track record of delivering software or models into production use by scientists, not just prototypes.
- 12+ years of relevant experience, including 5+ years leading technical teams; experience leading leaders strongly preferred.
- Experience with modalities beyond conventional small molecules — PROTACs and molecular glues, macrocycles, peptides, covalent inhibitors, or oligonucleotides;
- Familiarity with lab automation, self-driving lab concepts, or high-throughput chemistry integration;
- Experience evaluating or deploying large-scale pretrained models for chemistry or protein structure;
- Prior experience in a large matrixed pharma R&D organization, or scaling a capability from startup to enterprise;
- Strong external profile: publications, conference presence, or leadership in precompetitive consortia.
Bayer offers a wide variety of competitive compensation and benefits programs. If you meet the requirements of this unique opportunity, and want to impact our mission Health for all, Hunger for none, we encourage you to apply now. Be part of something bigger. Be you. Be Bayer.
To all recruitment agencies: Bayer does not accept unsolicited third party resumes.
Bayer is an Equal Opportunity Employer/Disabled/Veterans
Bayer is committed to providing access and reasonable accommodations in its application process for individuals with disabilities and encourages applicants with disabilities to request any needed accommodation(s) using the contact information below.
At Bayer we’re visionaries, driven to solve the world’s toughest challenges and striving for a world where 'Health for all Hunger for none’ is no longer a dream, but a real possibility. We’re doing it with energy, curiosity and sheer dedication, always learning from unique perspectives of those around us, expanding our thinking, growing our capabilities and redefining ‘impossible’. There are so many reasons to join us. If you’re hungry to build a varied and meaningful career in a community of brilliant and diverse minds to make a real difference, there’s only one choice.
Head of Machine Learning Research
We are seeking a scientific and technical leader to advance machine learning–enabled molecular design across our drug discovery portfolio. As a member of the Data Science & AI leadership team, you will define and deliver the models, platforms, and ways of working that shorten design–make–test–analyze cycles and improve the quality of molecules progressing toward nomination.
- Co-own the vision, roadmap, and investment case for ML-driven molecule design and multi-parameter optimization across small molecules and emerging modalities.
- Make build/buy/partner decisions across generative design, property prediction, structure-based ML, and synthesis planning; evaluate foundation models and external platforms with rigor rather than hype.
- Set the standard for how models are validated, benchmarked prospectively, and retired — including uncertainty quantification, applicability domain, and honest reporting of failure modes.
- Partner with Drug Discovery Sciences — medicinal chemistry, structural biology, biophysics, screening, DMPK, and safety — to embed ML into live programs from hit identification through candidate nomination.
- Co-own program-level design goals with chemistry leads: potency, selectivity, ADMET, developability, and IP position optimized together rather than sequentially.
- Drive active learning and closed-loop DMTA cycles, including integration with high-throughput and automated synthesis where available.
- Ensure models are delivered as reliable, supported products that chemists actually use, not one-off analyses.
- Direct development of core capabilities: ADMET and property predictors, generative and de novo design, retrosynthesis and synthetic accessibility, and structure-based ML including co-folding, pose prediction, docking rescoring, and ML-accelerated free energy methods.
- Work with data engineering to secure the assay, structural, and DMTA data foundation these models depend on — curation, provenance, harmonization across assays and sites, and feedback capture from every make-test cycle.
- Establish MLOps practice appropriate to a regulated R&D environment: versioning, reproducibility, monitoring, and documentation.
- Build, lead, and develop a team of approximately 25 spanning ML research, computational chemistry, and ML engineering; grow leaders within the group.
- Own budget, vendor relationships, and external collaborations with academic groups, consortia, and biotech partners.
- Represent the function to R&D leadership, translating technical capability into portfolio impact and translating portfolio priorities into technical strategy.
- Contribute to the broader scientific community through publication, presentation, and precompetitive collaboration where appropriate.
- PhD in computational chemistry, cheminformatics, structural biology, biophysics, computer science, or a related field, with demonstrated depth in both a molecular science and machine learning. Exceptional candidates with an MSc and equivalent depth of experience will be considered;
- Deep, current expertise across modern ML for molecules: graph neural networks, transformers, generative approaches (diffusion, flow matching, autoregressive), Bayesian optimization and active learning, transfer learning on sparse assay data, and uncertainty quantification;
- Working command of the discovery domain: SAR interpretation, multi-parameter optimization, ADMET and developability, protein structure and ligand binding, docking and free energy methods;
- Demonstrated impact on real programs — molecules advanced, cycles shortened, decisions changed. A publication record without program impact is not sufficient for this role;
- Proven ability to partner with medicinal chemists and structural biologists as scientific peers, including the credibility to challenge and be challenged on design decisions;
- Track record of delivering software or models into production use by scientists, not just prototypes.
- 12+ years of relevant experience, including 5+ years leading technical teams; experience leading leaders strongly preferred.
- Experience with modalities beyond conventional small molecules — PROTACs and molecular glues, macrocycles, peptides, covalent inhibitors, or oligonucleotides;
- Familiarity with lab automation, self-driving lab concepts, or high-throughput chemistry integration;
- Experience evaluating or deploying large-scale pretrained models for chemistry or protein structure;
- Prior experience in a large matrixed pharma R&D organization, or scaling a capability from startup to enterprise;
- Strong external profile: publications, conference presence, or leadership in precompetitive consortia.
Bayer offers a wide variety of competitive compensation and benefits programs. If you meet the requirements of this unique opportunity, and want to impact our mission Health for all, Hunger for none, we encourage you to apply now. Be part of something bigger. Be you. Be Bayer.
To all recruitment agencies: Bayer does not accept unsolicited third party resumes.
Bayer is an Equal Opportunity Employer/Disabled/Veterans
Bayer is committed to providing access and reasonable accommodations in its application process for individuals with disabilities and encourages applicants with disabilities to request any needed accommodation(s) using the contact information below.
See All 9 Machine Learning Research Jobs in Massachusetts
Find roles in Massachusetts that match your experience and apply in just a few clicks.
Find JobsMachine Learning Research Jobs by City in Massachusetts
Where Massachusetts roles are concentrated, by current openings.
Machine Learning Research Job Market in Massachusetts
A snapshot from current Massachusetts openings, updated as new roles post.
Who's Hiring
Top Industries Hiring
- Medical Devices
- Electronics & Hardware
What Massachusetts Employers Look For
The qualifications that appear most often in machine learning research jobs across Massachusetts.
- Master's or PhD in computer science, statistics, or a directly related quantitative field
- Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow
- Experience designing and training large-scale machine learning models on real datasets
- Published research or a demonstrable portfolio of applied ML projects
- Familiarity with cloud computing environments such as AWS, Google Cloud, or Azure
- Strong written and verbal communication skills for presenting research findings to cross-functional teams
Machine Learning Research Jobs in Massachusetts: Frequently Asked Questions
How do you become a machine learning research in Massachusetts?
Machine learning research roles in Massachusetts do not require a state-issued license, but employers consistently expect at minimum a master's degree, with many Cambridge and Boston research labs preferring a PhD in computer science, mathematics, or a related field. The typical path involves graduate study, a thesis or published work demonstrating independent research, and internship experience at a Massachusetts academic institution or industry lab before moving into a full-time research position.
Which companies hire machine learning researchs in Massachusetts?
Employers hiring machine learning researchs in Massachusetts right now include STR, suno, and Apple, based on current listings on Migrate Mate as of September 2026. Massachusetts's dense concentration of research universities and biotech corridors means many of these employers offer both applied industry positions and roles with an academic collaboration component.
Which Massachusetts cities have the most machine learning research jobs?
Boston, Milford, and Cambridge have the most machine learning research openings in Massachusetts. The Greater Boston area, including Cambridge, drives the bulk of demand through its university spin-offs, major research hospitals, and defense contractors, while Waltham and Burlington attract openings from established tech and life sciences companies that have clustered along the Route 128 corridor over several decades.
Are there remote machine learning research jobs in Massachusetts?
Yes, and more than most fields, since machine learning research work is fundamentally computational and does not require physical presence. About 0% of machine learning research openings tied to Massachusetts are remote or hybrid as of September 2026, reflecting how portable the work is. Modeling, experimentation, and paper writing transfer well to remote arrangements, though roles involving proprietary hardware, classified defense projects, or wet-lab data collection at Massachusetts research institutions typically remain on-site.
How can I get hired as a machine learning research in Massachusetts with little or no experience?
The most realistic entry path is a research internship or co-op at a Massachusetts university lab or teaching hospital, since institutions like Harvard, MIT, and Boston University regularly place students into paid research assistant roles that serve as direct pipelines to full-time positions. Candidates without a graduate degree can strengthen their profile by completing a rigorous applied ML portfolio, contributing to open-source projects, and targeting associate research scientist or junior ML engineer titles at mid-size biotech and robotics companies concentrated along Route 128, where competition is lower than at flagship Cambridge labs.
Where can I find and apply to machine learning research jobs in Massachusetts?
You can find and apply to machine learning research jobs in Massachusetts on Migrate Mate, which lists current Massachusetts openings in one place. Find roles that fit your background and apply directly to the employers posting them.
See All 9 Machine Learning Research Jobs in Massachusetts
Find roles in Massachusetts that match your experience and apply in just a few clicks.
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