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BigHat Biosciences is seeking a Machine Learning Scientist to advance AI/ML-driven therapeutic antibody design. The company operates a full-stack antibody drug development platform that integrates roboticized high-throughput wet-lab work with custom data management and ML orchestration, enabling accelerated discovery and optimization of complex therapeutics.
In this role, you will design and implement state-of-the-art generative models for antibody sequence and structure prediction, trained on proprietary internal datasets containing thousands to millions of antibodies. You'll develop multi-modality, multi-objective iterative protein sequence optimization approaches for lab-in-the-loop antibody design, with success measured by synthesis of real antibodies with drug-like properties in the wet lab.
Key responsibilities include developing and deploying agentic and LLM-driven optimization methods to automate and accelerate the design-build-test cycle, providing ML expertise to ongoing therapeutics programs, and collaborating with engineering teams to ensure efficient automated deployment of models. You'll work closely with an interdisciplinary team including drug developers, wet lab scientists, automation specialists, and data scientists across all therapeutics programs.
Required qualifications: PhD in ML, CS, EE, or relevant scientific discipline with hands-on experience developing and applying novel ML methods and strong quantitative background. Proficiency in Python, PyTorch, and modern software engineering practices (testing, CI/CD, agentic/LLM-assisted coding). Excellent communication skills and sufficient biomedical domain knowledge to interact effectively with diverse scientific teams. Energy and ambition to execute in a fast-paced environment across multiple projects.
Desirable experience includes de novo design, NGS data analysis, Bayesian optimization, antibody biology and drug development knowledge, AWS model training and deployment, and publications at major ML conferences.
