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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 ML Researchers to join our Biomolecular Emulator (BioEmu) and Small Molecules Drug Discovery project. The BioEmu project aims to model the dynamics and function of proteins --- how they change shape, bind to each other, and bind small molecules. Our BioEmu-1 model was published in Science. In our Small molecules - Microsoft Research work we build and apply large ML and LLM algorithms to accelerate the discovery of small molecule drugs and materials. Our team encompasses people from multiple disciplines across machine learning, engineering, and the natural sciences, who work together closely on well-defined and challenging goals. If you have strong machine learning expertise and enjoy designing and creating tools for scalable machine learning research for the natural sciences, please apply.
Job Responsibility:
Invent novel deep learning techniques for models of (bio)molecular structure, dynamics, reactivity and function
Design, implement, and iterate on model architectures and training algorithms (e.g., diffusion/sequence–structure models, language models, representation learning)
run rigorous ablations and baselines
Define success where standards don’t exist yet: proposing sound benchmarks and uncertainty‑aware metrics that reflect real‑world utility
Build high‑quality research code (Python/PyTorch) with reproducible workflows and robust data pipelines
Partner across disciplines—communicate clearly with ML researchers and experimental/computational biologists/chemists
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)
Requirements:
PhD or equivalent research experience in Computer Science, Machine Learning, Physics, Chemistry, 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 problems in biology or chemistry
Comfort with real‑world, noisy/heterogeneous data
Nice to have:
ML Engineering skills (e.g., model optimization and deployment, code design, CUDA)
Experience with geometric DL, reinforcement learning, generative models or large language models
Experience with biomolecular modeling or bioinformatics (e.g., folding systems, structural analysis/visualization, MD simulation, structure/genome databases) and/or chemical modelling (2D/3D structures), chemoinformatics and computational chemistry
Ability to work with and interpret real‑world biological data (e.g., cryo‑EM, protein binding affinities, structural/biophysical/chemical measurements, molecular data)