Head of Machine Learning Research (all genders)

We are seeking a scientific and technical leader to make AI & machine learning central to how we design and optimize molecules across our discovery portfolio. You will sit on the Data Science & AI leadership team and be accountable for the models, platforms, and ways of working that shorten design-make-test-analyze cycles and raise our probability of delivering high-quality candidates.

This is a hands-on strategy role, not research-only or delivery-only. You will set a multi-year technical roadmap, build and lead a team of 25, and be measured by whether project teams make better decisions and reach candidate faster because of the capabilities you build. It demands fluency in both modern AI/ML and the medicinal chemistry and structural biology realities of drug discovery — expect to spend as much time in project reviews as in model architecture discussions.

• *YOUR TASKS AND RESPONSIBILITIES**
• You define and deliver an AI/ML molecular design strategy aligned with current and future Drug Discovery priorities
• You make rigorous build/buy/partner decisions for generative design, property prediction, structure-based ML, synthesis planning, foundation models, and external platforms
• You establish robust model-validation standards, including prospective benchmarking, uncertainty quantification, applicability domains, reproducibility, and transparent reporting of limitations
• You monitor the external AI/ML landscape, identify disruptive developments early, and secure access to best-in-class capabilities
• You embed ML into live discovery programs by partnering closely with medicinal chemistry, computational design, structural biology, biophysics, screening, DMPK, safety, and Drug Discovery Sciences
• You co-own program outcomes from hit identification through candidate nomination, optimizing potency, selectivity, ADMET, developability, synthetic feasibility, and IP position
• You lead development of core platforms for ADMET/property prediction, generative and de novo design, retrosynthesis, synthetic accessibility, co-folding, pose prediction, docking rescoring, and ML-accelerated free-energy methods
• You build a scalable data and MLOps foundation with curated and harmonized assay, structural, and DMTA data; ensuring provenance, feedback capture, versioning, monitoring, documentation, and reproducibility
• You lead and develop a high-performing interdisciplinary organization; managing strategic investments and external partnerships while fostering scientific excellence, collaboration, and measurable portfolio value
• *WHO YOU ARE**
• You hold a PhD in computational chemistry, cheminformatics, structural biology, biophysics, computer science, or a related field, with demonstrated depth in both molecular science and machine learning. Exceptional MSc candidates with equivalent experience will be considered
• You bring extensive relevant experience, including strong exposure to leading technical teams; experience leading leaders is strongly preferred
• You have experience in a large matrixed pharma R&D organization, or scaling a capability from startup to enterprise
• You possess deep, current expertise in ML for molecules: graph neural networks, transformers, generative methods (diffusion, flow matching, autoregressive), Bayesian optimization and active learning, transfer learning on sparse assay data, and uncertainty quantification
• You have a working command of the discovery domain: SAR interpretation, multi-parameter optimization, ADMET and developability, protein structure and ligand binding, docking and free energy methods
• You demonstrate impact on real programs, with advanced molecules, cycles shortened, and decisions changed, a publication record alone is not sufficient
• You have the credibility to partner with medicinal chemists and structural biologists as a scientific peer, and to challenge and be challenged on design decisions
• You have a track record of delivering models and software into production use by scientists, not just prototypes
• You bring experience beyond conventional small molecules: PROTACs and molecular glues, macrocycles, peptides, covalent inhibitors, or oligonucleotides
• You maintain a strong external profile: publications, conference presence, or leadership in precompetitive consortia
• You speak English fluently
• *WHAT WE OFFER**

Our benefits package is flexible, appreciative, and tailored to your lifestyle, because: **What matters to you, matters to us!**

• We ensure your financial **stability** with a competitive salary between 129.700€ and 163.800€ per year (full-time) plus a variable component and a global equity-based cash plan. Your compensation is based on your qualifications, skills, and professional experience.
• Your **family** is a top priority. We offer loving company daycare centers at multiple locations, support in finding childcare, time off for the care of elderly or dependent family members, summer camps for children, and much more.
• We support your professional **growth** by providing access to learning and development opportunities, training programs through the Bayer Learning Academy, development dialogues, as well as coaching and mentoring programs.
• We promote **health** awareness and opportunities for selfcare through various measures, such as free health checks with the company doctor.
• We emb  **diversity** by providing an inclusive work environment in which you are welcomed, supported, and encouraged to bring your whole self to work.
• *Be You. Be Bayer**

#LI-AMSEMEA

• *YOUR APPLICATION**

This is your opportunity to tackle the world’s biggest challenges with us: Maintaining our health, feeding growing populations and slowing the rate of climate change. You have a voice, ideas and perspectives and we want to hear them. Because our success begins with you. Be part of something big. Be Bayer.

Bayer welcomes applications from all individuals, regardless of , national origin, gender, age, physical characteristics, social origin, disability, union membership, religion, family status, , orientation, gender identity, gender expression or any criterion under applicable law. We are committed to treating all applicants fairly and avoiding .

• *Location:** ​​ ​   Germany : Berlin : Berlin​

​​**Division:** ​               Pharmaceuticals​

• *Reference Code:** 885871​

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Veröffentlicht am 01. Nov. 2026

Veröffentlicht am 01. Nov. 2026

Quelle: Statistisches Bundesamt (Destatis), Tabelle; dl-de/by-2-0; eigene Berechnung