Senior Researcher/Engineer: AI for biomolecular modelling and simulation in London

The Human Genome Project made human protein sequences accessible at scale; AlphaFold did the same for structure prediction. But our understanding of biomolecular function is still in its infancy, limiting our ability to develop safe, effective medicines.

Our aim is to solve biomolecular function at scale. To do so, we develop AI emulators that model how biomolecules change structure and form complexes, how they interact with drug molecules and nucleic acids, how they change over time and catalyse reactions. Our vision is to simulate molecular processes from the ground up, with quantum chemical accuracy, and using AI to generate orders-of-magnitude higher throughputs than traditional simulation or experimentation. Some of our recent models include BioEmu1, BioEmu2, RetroChimera and Skala.

We invite you to join us on this mission. We are seeking candidates with backgrounds in Machine Learning, Computational Chemistry, Physics or Biology. You will be working in a highly dynamic and interdisciplinary team, developing models, algorithms and helping to orchestrate large-scale data generation campaigns and training runs. If you are passionate about this research area and believe you can make a lasting impact, we would love to receive your application.

This post will be open until the position is filled. When submitting your application, include your CV with a list of publications and other scientific or engineering contributions such as references to developed software.

Responsibilities

• Contribute to and drive a high-impact, research agenda development on machine learning in the molecular sciences.
• Develop strategies for integrating large-scale heterogeneous model data from molecular simulation and laboratory experiments and identifying untapped data sources.
• Write research code to test new approaches or develop novel theoretical and practical insights.
• Prepare technical papers and presentations.
• Work on a day-to-day basis with an international and interdisciplinary research team on one overarching research goal.
• Be fully committed to one research project that may take several years to come to fruition and prioritise team success over individual research interests.

Qualifications

• Experienced in Python software development with object-oriented design and basic deployment, ideally demonstrated by published software projects (e.g., github).
• Experienced in developing and implementation of deep learning systems (e.g., in PyTorch or JAX) and agentic software design and testing.
• Experienced in modelling or simulation of molecules, ideally biomolecules.
• High-quality publications in leading disciplinary or interdisciplinary venues or demonstrated leadership in software development.

Preferred

• Experience with protein science, bioinformatics, biophysics or computational Chemistry.
• Understanding of computational statistics and experience with generative model development (e.g., diffusion or flow-models).
• Experience in method development for molecular dynamics and statistical mechanics.
• Track record of developing and optimising novel deep learning architectures.

This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, colour, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.