Principal, Machine Learning Scientist
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Job overview
BigHat Biosciences is hiring a Principal, Machine Learning Scientist. The role seeks a creative, accomplished Principal Machine Learning Scientist to advance ML‑driven therapeutic antibody design, applying world‑class ML skills to refine protein engineering platforms, lead research strategy, and collaborate across interdisciplinary drug development teams at BigHat Biosciences.
Key focus areas include Design and implement state‑of‑the‑art generative and predictive antibody models, Provide leadership, technical guidance, and mentorship to ML and data science staff, and Help set strategy for future ML research aligned with drug development challenges.
Important skills include PyTorch, Machine Learning, Biology, Generative Models Of Antibody Sequence And Structure, Predictive Models Of Antibody Properties, and Leadership. Preferred (not required): Drug Development, Protein Sciences, Research, and Python.
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Full job description
The role: We are seeking a creative, accomplished Principal Machine Learning Scientist to advance the state of the art in ML-driven therapeutic antibody design. At BigHat Biosciences our full-stack antibody drug development platform uses ML to drive every stage from discovery to optimization. Our roboticized high-throughput wet-lab continually adds to our large proprietary datasets, which are piped through a custom LIMS++ data management and orchestration layer to automatically update and deploy the latest models. This makes the development of complex, next-gen therapeutics ‘trivially parallelizable’, at a pace which only accelerates as we develop better ML tooling. You’re not interested in just git-cloning the latest NeurIPS pub and swapping out the dataset. Motivated by an enthusiasm for the possibility of addressing unmet patient needs and a curiosity about the underlying biology, you’ll apply your world-class ML skillset to refine and expand this state-of-the-art protein engineering platform. Success will mean not only hands-on methods development, but helping shape the direction for future ML research, and actively participating in the application of our platform to the accelerated design of new therapeutics.
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