Head of ML Research for Molecule Design

Bayer (Schweiz) AG; Cambridge, Northern (MA, KY); Hybrid; USD 177,000 - 266,000; Full time

Job summary

Bayer seeks a Head of Machine Learning Research to lead ML-driven molecule design across drug discovery. You will co-own the multi-year roadmap, build a team of about 25 scientists and engineers, and ensure ML capabilities translate into faster, better decisions in discovery.

The role blends strategic leadership with hands-on model work, spanning graph neural networks, generative models, and uncertainty quantification, in collaboration with medicinal chemistry and biology teams.

Qualifications

• PhD in computational chemistry, cheminformatics, structural biology, biophysics, CS or related field.
• Deep, current expertise in ML for molecules: GNNs, transformers, generative methods, Bayesian optimization, uncertainty quantification.
• Discovery domain familiarity: SAR, multi-parameter optimization, ADMET, protein structure, docking and free energy methods.

Responsibilities

• Co-own the ML-driven molecule design strategy, roadmap, and investment.
• Deliver ML products into live drug discovery programs with chemistry and biology teams.
• Lead platform/data foundation for ML: property predictors, retrosynthesis, and structure-based ML.
• Grow and manage a 25-person team; manage budgets and external collaborations.

Skills

Graph neural networks

Transformers

Generative models

Uncertainty quantification

Active learning

Molecule design

SAR interpretation

ADMET & developability

Protein structure & docking

Production-ready ML

Education

PhD in computational chemistry/cheminformatics/structural biology/biophysics/CS

MSc with depth in molecular ML or related field

Tools

Lab automation

Pretrained models evaluation

Job description

Bayer seeks a Head of Machine Learning Research to lead ML-driven molecule design across drug discovery. You will co-own the multi-year roadmap, build a team of about 25 scientists and engineers, and ensure ML capabilities translate into faster, better decisions in discovery.

The role blends strategic leadership with hands-on model work, spanning graph neural networks, generative models, and uncertainty quantification, in collaboration with medicinal chemistry and biology teams.