Principal Machine Learning Scientist · Bayer

Wed, 29 Apr 2026 15:06:36 GMT
Engineering

Bayer · Cambridge · $123,760.00 - $185,640.00 · onsite

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.

Your Tasks and Responsibilities

The primary responsibilities of the Principal Machine Learning Scientist are to:

Develop, evaluate, and apply machine learning algorithms and workflows for accelerating early-stage drug discovery, including but not limited to (i) de-novo design of biomolecules, (ii) assessment of target druggability across therapeutic modalities (iii) design of drug delivery systems, (iv) identification of novel druggable pockets and epitopes, (vi) characterization of protein-protein and protein-ligand interactions;

Contribute to the implementation, validation, and improvement of machine learning tools and software solutions that support drug discovery activities;

Identify opportunities for accelerating ongoing drug discovery projects with internal and external AI capabilities;

Communicate, educate, and engage with a broad set of stakeholders (chemists, biologists, computational/data scientists, R&D leadership) on the state of technology and the progress of key internal initiatives. Engage with the broader scientific community through publications, talks, and open-source;

Keep up to date with the latest advances in AI-driven modeling of biomolecular structure and dynamics.

Who You Are

Bayer seeks an incumbent who possesses the following:

Ph.D. degree in Computational Chemistry/Biology, Chem/Bioinformatics, Chemical/Biological/Molecular Engineering, or a related field at the intersection of life sciences and computer science;

Deep expertise with state-of-the-art machine learning methods for modeling biomolecules, like co-folding and/or generative methods for protein design;

Expertise in handling, processing, integrating and analyzing large datasets related to drug development research, including biochemical, biophysical, and structural biology data;

Strong programming skills in Python;

Demonstrated commitment to scientific rigor, a track record of scientific excellence, strong analytical thinking, and a high degree of self-motivation;

Excellent written and verbal communication.

Preferred Qualifications

5+ years of relevant post-PhD experience, including 2+ years in industry;

Experience with established, physics-based protein modelling methods like Molecular Dynamics and/or Rosetta;

Experience in coordinating small, interdisciplinary teams and ability to articulate their impact to managerial stakeholders;

Strong record of publications or patents related to machine learning solutions for biomolecular modeling.