Principal, Machine Learning Scientist

Key Responsibilities

• 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.

Skills Knowledge and Expertise

• 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.

Total Rewards

• Range of health insurance plan options through Anthem and Kaiser (monthly credit if benefit waived)
• Dental, and vision coverage through Guardian
• Additional well-being benefits through Nayya, OneMedical, Wagmo, Rula, and more
• 401(k) with company match 
• DTO, two weeks of company-wide shutdown, and 12 company holidays
• Paid parental leave

About
BigHat Biosciences designs safer, more effective biologic therapies for patients using machine learning and synthetic biology. BigHat integrates a wet lab for high-speed characterization with machine learning technologies to guide the search for better antibodies. We apply these design capabilities to develop new generations of safer and more effective treatments for patients suffering from today’s most challenging diseases.BigHat is a Series B biotech outside San Francisco with a team-oriented, inclusive, and family-friendly culture. Our broad pipeline of wholly-owned and partnered therapeutic programs span many disparate indications with high unmet need, such as cancer, inflammation, and infectious disease. BigHat has raised >$100M from top investors, including Section 32, a16z, and 8VC.