ML Research Engineer Jobs in Massachusetts
ML Research Engineer jobs in Massachusetts are among the most active in the country, concentrated in life sciences AI, robotics, and large language model development, with demand from entry-level research associates through principal scientists. Boston, Cambridge, and the Route 128 corridor account for the largest share of openings, anchored by employers such as MIT Lincoln Laboratory, Google, and Moderna. Model interpretability, reinforcement learning, and multimodal systems are the specialties drawing the most consistent interest from Massachusetts hiring teams. Find a role that fits below and apply directly.
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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 8 ML Research Engineer Jobs in Massachusetts
Find roles in Massachusetts that match your experience and apply in just a few clicks.
Find ML Research Engineer JobsML Research Engineer Jobs by City in Massachusetts
Where Massachusetts roles are concentrated, by current openings.
ML Research Engineer Job Market in Massachusetts
A snapshot from current Massachusetts openings, updated as new roles post.
Who's Hiring
Top Industries Hiring
- Medical Devices
What Massachusetts Employers Look For
The qualifications that appear most often in ML research engineer jobs across Massachusetts.
- PhD or MS in machine learning, computer science, statistics, or a closely related field
- Demonstrated research output such as peer-reviewed publications or conference papers at NeurIPS, ICML, or ICLR
- Strong proficiency in Python and deep learning frameworks including PyTorch or JAX
- Experience designing and running large-scale experiments on distributed computing infrastructure
- Ability to translate research findings into production-ready models or prototypes for applied teams
- Familiarity with responsible AI principles, model evaluation, and bias mitigation methodologies
ML Research Engineer Jobs in Massachusetts: Frequently Asked Questions
How do you become a ml research engineer in Massachusetts?
Most ml research engineers in Massachusetts enter the field through a graduate degree, typically a master's or PhD in machine learning, computer science, or a related discipline from institutions such as MIT, Harvard, or Boston University. The role carries no state-issued license or board registration in Massachusetts. Employers here weigh research output heavily, so a strong publication record or a well-documented project portfolio is the most direct path to clearing the initial screening.
Which companies hire ml research engineers in Massachusetts?
Employers hiring ml research engineers in Massachusetts right now include STR, suno, and Bayer, based on current listings on Migrate Mate as of September 2026. Massachusetts's dense concentration of biotech, defense technology, and AI software firms means openings appear across a broader range of industries than in most other states.
Which Massachusetts cities have the most ml research engineer jobs?
Boston, Milford, and Waltham have the most ml research engineer openings in Massachusetts. Boston and Cambridge dominate because of the research universities, venture-backed AI startups, and major tech campuses concentrated there, while suburban Route 128 cities attract additional openings from established defense contractors and biopharma companies that maintain large R&D operations outside the urban core.
Are there remote ml research engineer jobs in Massachusetts?
Yes, and more than most fields. About 0% of ml research engineer openings tied to Massachusetts are remote or hybrid as of September 2026, reflecting how much of the work involves coding, experimentation, and analysis rather than on-site equipment. Fully remote positions tend to concentrate in pure research and modeling roles, while positions requiring access to proprietary hardware or lab infrastructure are more likely to require on-site presence.
How can I get hired as a ml research engineer in Massachusetts with little or no experience?
The most realistic entry path is a research internship or a co-op position, which MIT, Northeastern, and Boston University facilitate through structured industry partnerships with Massachusetts employers. Candidates without a graduate degree can target research engineer associate roles at companies like Raytheon Technologies or Dana-Farber Cancer Institute that hire strong undergraduates for applied ML support work. A documented GitHub portfolio showing original model implementations or replicated papers from recent top-tier conferences gives candidates without industry history a concrete edge in Massachusetts screening processes.
Where can I find and apply to ml research engineer jobs in Massachusetts?
You can find and apply to ml research engineer jobs in Massachusetts on Migrate Mate, which lists current Massachusetts openings across industries and experience levels. Find roles that fit your background and apply directly to the employers posting them.
See All 8 ML Research Engineer Jobs in Massachusetts
Find roles in Massachusetts that match your experience and apply in just a few clicks.
Find ML Research Engineer Jobs