Associate Director/Principal, Machine Learning Scientist

The role: We are seeking a creative, accomplished Assoc. Director or 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. Design and implement the next state-of-the-art generative models of antibody sequence and structure, and predictive models of antibody properties, trained on proprietary internal datasets of thousands to millions of antibodies.

Provide leadership, technical guidance, and mentorship to other ML and data science FTEs and interns. Help set strategy for future ML research, driven by a strong high-level understanding of BigHat programs and operations as well as real-world drug development challenges. Develop, refine, and deploy de novo design methods for generating initial hits to challenging, therapeutically interesting targets.

Develop multi-modality, multi-objective iterative protein sequence optimization approaches to lab-in-the-loop antibody design problems for validation and deployment in our high-throughput wet lab - at BigHat success is only declared upon synthesis of real antibodies with drug-like properties. Maintain an in-depth understanding of the current state-of-the-art in ML-driven protein engineering, both in the literature and at BigHat. Share your findings at top-tier conferences and publish in leading scientific journals to advance the field of protein engineering.

Provide ML expertise and support for ongoing therapeutics programs, directly contributing to the development of new drugs. Collaborate with our engineering team to ensure maximal efficiency in the automated and agentic deployment of our latest models to our therapeutics programs. Work closely with an interdisciplinary team of drug developers, wet lab scientists, automation specialists, data scientists, etc.

to identify inefficiencies or potential improvements in BigHat’s platform, and plan and prioritize ML methods development accordingly. PhD in ML/CS or in the hard sciences with 5+ years experience post-graduation in developing and applying novel ML methods, and a strong quantitative background. Publications in major ML conferences and/or leading journals, and an extensive demonstrable track record developing and applying novel ML in industry.

Strong competency in Python, familiarity with PyTorch, and experience with modern software engineering best practices. Excellent communication skills, sufficient biomedical domain knowledge to interact effectively with diverse scientific teams. Enjoys a fast-paced environment and excels at executing across multiple projects.

Familiarity with the current state-of-the-art in ML-driven protein engineering Nice-to-haves include experience with de novo design, NGS data, Bayesian optimization, familiarity with antibody biology and drug development, and experience training and deploying models on AWS.