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BigHat Biosciences is seeking a Machine Learning Engineer to advance AI/ML-driven therapeutic antibody design. The company operates a full-stack antibody drug development platform that uses machine learning at every stage from discovery to optimization, integrated with a roboticized high-throughput wet lab and custom data management layer.
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'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 loop, providing ML expertise for ongoing therapeutics programs that directly contribute to new drug development, and collaborating with the engineering team to ensure efficient automated deployment of models and methods. You'll work closely with an interdisciplinary team including drug developers, wet lab scientists, automation specialists, and data scientists.
Required qualifications: Master's degree in ML/CS/EE or Bachelor's with 3+ years of industry experience. Strong hands-on experience developing and applying novel ML methods with a quantitative background. Proficiency in Python and PyTorch, with familiarity with modern software engineering practices including testing and CI/CD. Excellent communication skills and sufficient biomedical domain knowledge to interact effectively with diverse scientific teams. Energy and ambition to thrive in a fast-paced environment executing across multiple projects.
Nice-to-haves include experience with de novo design, NGS data, Bayesian optimization, antibody biology and drug development knowledge, AWS model training and deployment, and publications at major ML conferences.
