{"id":1887263,"url":"https://alion.io/job/bayer-head-of-machine-learning-research","title":"Head of Machine Learning Research","company":{"id":671164,"name":"Bayer","domain":"bayer.com","url":"https://alion.io/company/bayer-7","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":{"grade":"A","score":100,"open_postings":18,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":11,"computed_at":"2026-11-03T05:54:40Z"}},"role":"Leadership","role_family":"Leadership","seniority":"head","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Cambridge, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":177400,"max":266200,"currency":"USD","period":"year","gross":null,"usd_annual":266200},"salary_estimate":null,"experience_years_min":12,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"Transfer Learning","optional":false}],"status":"live","first_seen_at":"2026-10-05T03:07:18Z","employer_posted_date":"2026-10-21","last_verified_at":"2026-11-03T04:19:15Z","board_verified":true,"closed_at":null,"days_open":29,"trust":{"level":"ok","repost_count":1,"flags":[],"days_open":29},"description":"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.\nHead of Machine Learning Research\nWe 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.\nThis is a hands-on strategic leadership role -neither research-only nor delivery-only. You will set a multi-year technical roadmap, build and lead a team of approximately 25 scientists and engineers, and ensure that machine learning capabilities translate into better project decisions and faster progression to candidate selection.\nYou will lead the development and application of modern machine learning methods-including deep learning, neural networks, ensemble methods, natural language processing, and machine perception-to complex, high-dimensional scientific data. Your team will develop predictive and generative models that help discovery scientists understand biological and chemical patterns, prioritize experiments, optimize molecular properties, and design novel molecules.\nSuccess in this role requires deep fluency in contemporary machine learning, combined with a practical understanding of the realities of medicinal chemistry, structural biology, and drug discovery. You will spend as much time engaging in project-team reviews and scientific decision-making as you do shaping model architectures, platform strategy, and technical standards\nYOUR TASKS AND RESPONSIBILITIES\nThe primary responsibilities of this role, Head of Machine Learning Research, are to:\nStrategy and Technical Direction\nCo-own the vision, roadmap, and investment case for ML-driven molecule design and multi-parameter optimization across small molecules and emerging modalities.\nMake 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.\nSet the standard for how models are validated, benchmarked prospectively, and retired - including uncertainty quantification, applicability domain, and honest reporting of failure modes.\nDelivery into Discovery Projects\nPartner 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.\nCo-own program-level design goals with chemistry leads: potency, selectivity, ADMET, developability, and IP position optimized together rather than sequentially.\nDrive active learning and closed-loop DMTA cycles, including integration with high-throughput and automated synthesis where available.\nEnsure models are delivered as reliable, supported products that chemists actually use, not one-off analyses.\nPlatform and Data Foundation\nDirect 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.\nWork 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.\nEstablish MLOps practice appropriate to a regulated R&D environment: versioning, reproducibility, monitoring, and documentation.\nLeadership and Organization\nBuild, lead, and develop a team of approximately 25 spanning ML research, computational chemistry, and ML engineering; grow leaders within the group.\nOwn budget, vendor relationships, and external collaborations with academic groups, consortia, and biotech partners.\nRepresent the function to R&D leadership, translating technical capability into portfolio impact and translating portfolio priorities into technical strategy.\nContribute to the broader scientific community through publication, presentation, and precompetitive collaboration where appropriate.\nWHO YOU ARE\nBayer seeks an incumbent who possesses the following:\nRequired Qualifications:\nPhD 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;\nDeep, 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;\nWorking command of the discovery domain: SAR interpretation, multi-parameter optimization, ADMET and developability, protein structure and ligand binding, docking and free energy methods;\nDemonstrated impact on real programs - molecules advanced, cycles shortened, decisions changed. A publication record without program impact is not sufficient for this role;\nProven ability to partner with medicinal chemists and structural biologists as scientific peers, including the credibility to challenge and be challenged on design decisions;\nTrack record of delivering software or models into production use by scientists, not just prototypes.\nPreferred Qualifications:\n12+ years of relevant experience, including 5+ years leading technical teams; experience leading leaders strongly preferred.\nExperience with modalities beyond conventional small molecules - PROTACs and molecular glues, macrocycles, peptides, covalent inhibitors, or oligonucleotides; \nFamiliarity with lab automation, self-driving lab concepts, or high-throughput chemistry integration;\nExperience evaluating or deploying large-scale pretrained models for chemistry or protein structure;\nPrior experience in a large matrixed pharma R&D organization, or scaling a capability from startup to enterprise;\nStrong external profile: publications, conference presence, or leadership in precompetitive consortia.\nEmployees can expect to be paid a salary between $177,400.00 - $266,200.00. Additional compensation may include a bonus or commission (if relevant). Additional benefits include health care, vision, dental, retirement, PTO, sick leave, etc.\nThis salary range is merely an estimate and may vary based on an applicant’s location, market data/ranges, an applicant’s skills and prior relevant experience, certain degrees and certifications, and other relevant factors.\nThis posting will be available for application until at least 09/29/2026.\n\n YOUR APPLICATION\n\nBayer 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.\nTo all recruitment agencies: Bayer does not accept unsolicited third party resumes.\nBayer is an Equal Opportunity Employer/Disabled/Veterans\nBayer 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.\n\nEqual Opportunity Employer Statement: Notice for U.S. Visitors: All information on this site is subject to compliance with local rule and regulations as they may vary from time to time and across different geographies, including, without limitation, U.S. Executive Orders.\n\nBayer is an E-Verify Employer.\n\nLocation:\nUnited States : Massachusetts : Cambridge || United States : New Jersey : Whippany \n\nDivision:\nPharmaceuticals \n\nReference Code:\n885870 \n\nContact Us\n\n 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