EPM Scientific
Machine Learning Research Scientist (San Francisco County)
EPM Scientific, San Francisco, California, United States, 94133
Machine Learning Research Scientist
Our client is pioneering a new era in drug discovery by leveraging cutting-edge AI foundation models to design molecules. Backed by world-class investors and led by researchers with landmark contributions in AI-for-biology, this team is transforming how medicines are created. This is not a typical research role. You'll join a small, elite team at a pivotal stage, moving beyond protein structure prediction into real-world therapeutic engineering. On this team, you will directly shape next-generation AI models for antibody and drug design, validated at scale in wet-lab environments.
Responsibilities/Role Design and implement novel AI architectures for molecular and protein modeling. Translate open-ended biological questions into tractable computational experiments. Analyze large-scale biological datasets, define meaningful metrics, and visualize results for diverse audiences. Collaborate closely with wet-lab scientists, software engineers, and product teams to iterate rapidly from concept to impact. You'll help push the boundaries of AI-driven drug discovery, creating models that could redefine therapeutic design globally.
Qualifications: D. or equivalent experience in Machine Learning, Computational Biology, Bioinformatics, Computational Chemistry, or related fields. Publications or projects in top-tier ML or life-science venues (e.g., NeurIPS, ICML, Nature Methods). Strong Python skills and deep-learning frameworks (PyTorch, TensorFlow). Experience training and evaluating large models on protein, antibody, or small-molecule data-or transferable ML expertise. Ability to structure complex scientific questions into rigorous modeling workflows.
Our client is pioneering a new era in drug discovery by leveraging cutting-edge AI foundation models to design molecules. Backed by world-class investors and led by researchers with landmark contributions in AI-for-biology, this team is transforming how medicines are created. This is not a typical research role. You'll join a small, elite team at a pivotal stage, moving beyond protein structure prediction into real-world therapeutic engineering. On this team, you will directly shape next-generation AI models for antibody and drug design, validated at scale in wet-lab environments.
Responsibilities/Role Design and implement novel AI architectures for molecular and protein modeling. Translate open-ended biological questions into tractable computational experiments. Analyze large-scale biological datasets, define meaningful metrics, and visualize results for diverse audiences. Collaborate closely with wet-lab scientists, software engineers, and product teams to iterate rapidly from concept to impact. You'll help push the boundaries of AI-driven drug discovery, creating models that could redefine therapeutic design globally.
Qualifications: D. or equivalent experience in Machine Learning, Computational Biology, Bioinformatics, Computational Chemistry, or related fields. Publications or projects in top-tier ML or life-science venues (e.g., NeurIPS, ICML, Nature Methods). Strong Python skills and deep-learning frameworks (PyTorch, TensorFlow). Experience training and evaluating large models on protein, antibody, or small-molecule data-or transferable ML expertise. Ability to structure complex scientific questions into rigorous modeling workflows.