Senior ML Engineer Jobs in Massachusetts

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

• 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.