Share job

At Microsoft Research AI for Science, we believe machine learning and artificial intelligence has the potential to transform scientific modelling and discovery crucial for solving the most pressing problems facing society including sustainable materials and discovery of new drugs.

We seek a highly motivated Senior Researcher to join our Biomolecular Emulator (BioEmu) team. The BioEmu project aims to model the dynamics and function of proteins - how they change shape, bind to each other, and bind small molecules. This approach will help us to understand biological function and dysfunction on a structural level and lead to more effective and targeted drug discovery. Our BioEmu-1 model was published in Science (see our blog post for links to our open-source software and other resources and this explainer video).

Required:

• PhD or equivalent research experience in Computer Science, Machine Learning, Physics, or a related field.
• Demonstrated leadership in ML architecture and algorithm design.
• Strong expertise in deep learning (model design, large-scale training, evaluation and reproducibility), statistics and linear algebra.
• Proficiency in Python and modern ML/scientific frameworks (e.g., PyTorch, JAX, TensorFlow, NumPy, SciPy, Pandas).
• Peer-reviewed publications in leading venues (e.g., NeurIPS, ICML, ICLR or leading journals).
• Excellent technical communication for collaborating in an interdisciplinary team.
• Curiosity and drive to apply deep learning to biological problems.
• Comfort with real‑world, noisy/heterogeneous data.

Preferred:

• ML Engineering skills (e.g., model optimization and deployment, code design, CUDA).
• Experience with biomolecular modeling or bioinformatics (e.g., folding systems, structural analysis/visualization, MD simulation, structure/genome databases).
• Ability to work with and interpret real‑world biological data (e.g., cryo‑EM, protein binding affinities, structural/biophysical measurements).

#Research #AI for Science

• Invent novel deep learning techniques for models of biomolecular structure, dynamics, and function.
• Design, implement, and iterate on model architectures and training algorithms (e.g., diffusion/sequence–structure models, representation learning); run rigorous ablations and baselines.
• Define success where standards don’t exist: proposing sound benchmarks and uncertainty‑aware metrics that reflect real‑world utility.
• Build high‑quality research code (Python/PyTorch) with reproducible work-flows and robust data pipelines.
• Partner across disciplines—communicate clearly with ML researchers and experimental/computational biologists; present results and influence direction.
• Work autonomously and as a team player, reporting insights, risks, and next steps with crisp written/visual summaries.
• Thrive with imperfect, heterogeneous data, using principled curation, augmentation, and probabilistic evaluation.
• Aim for impact: try ideas quickly and fail-fast when they don't work. Rapidly convert working ideas to artifacts others can use (code, models, datasets, papers, patents).