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BigHat Biosciences is seeking an Associate Director or Principal Machine Learning Scientist to lead the advancement of ML-driven therapeutic antibody design. The company operates a full-stack antibody drug development platform that integrates machine learning at every stage from discovery to optimization, backed by a roboticized high-throughput wet lab that continuously generates proprietary datasets.
In this role, you will design and implement state-of-the-art generative models for antibody sequence and structure, as well as predictive models for antibody properties, trained on proprietary internal datasets containing thousands to millions of antibodies. You will provide technical leadership, guidance, and mentorship to other ML and data science team members, helping shape the strategic direction of ML research initiatives.
Key responsibilities include developing de novo design methods for generating initial hits to therapeutically challenging targets, creating multi-modality, multi-objective iterative protein sequence optimization approaches for lab-in-the-loop antibody design, and maintaining deep knowledge of state-of-the-art ML-driven protein engineering. You will validate methods through synthesis of real antibodies with drug-like properties in BigHat's high-throughput wet lab.
You will share findings at top-tier conferences and publish in leading scientific journals, provide ML expertise to ongoing therapeutics programs, and collaborate with engineering teams to ensure efficient automated deployment of models. Working across an interdisciplinary team of drug developers, wet lab scientists, automation specialists, and data scientists, you will identify platform inefficiencies and prioritize ML methods development accordingly.
Required qualifications include a PhD in ML/CS or hard sciences with 5+ years post-graduation experience developing and applying novel ML methods, strong quantitative background, and publications in major ML conferences or leading journals. You must demonstrate strong Python competency, familiarity with PyTorch, and modern software engineering best practices. Excellent communication skills and sufficient biomedical domain knowledge to interact effectively with diverse scientific teams are essential. Nice-to-haves include experience with de novo design, NGS data, Bayesian optimization, antibody biology, drug development, and AWS model deployment.
