Description
We are seeking a 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 machine learning to drive every stage from discovery to optimization. A roboticized high-throughput wet-lab continually adds to our large proprietary datasets, which are managed and orchestrated through a custom LIMS++ layer to automatically update and deploy the latest models.
As a Principal Machine Learning Scientist, you will apply your world-class ML skillset to refine and expand this state-of-the-art protein engineering platform. You will help shape the direction for future ML research and participate in applying the platform to accelerate the design of new therapeutics.
Key Responsibilities
- Design and implement next-generation generative models of antibody sequence and structure, and predictive models of antibody properties, trained on proprietary internal datasets.
- Provide leadership, technical guidance, and mentorship to other ML and data science team members.
- Help set the strategy for future ML research, driven by a strong understanding of BigHat programs and operations.
- Develop, refine, and deploy de novo design methods for generating initial hits to challenging targets.
- Develop multi-modality, multi-objective iterative protein sequence optimization approaches for lab-in-the-loop antibody design.
- Maintain an in-depth understanding of the current state-of-the-art in ML-driven protein engineering.
- Share findings at top-tier conferences and publish in leading scientific journals.
- Provide ML expertise and support for ongoing therapeutics programs.
- Collaborate with the engineering team to ensure efficient deployment of models.
- Work closely with an interdisciplinary team to identify inefficiencies and plan ML methods development.
Skills, Knowledge & Expertise
- PhD in ML/CS or hard sciences with 5+ years of experience in developing and applying novel ML methods.
- Publications in major ML conferences and/or leading journals.
- Strong competency in Python, familiarity with PyTorch, and experience with modern software engineering best practices.
- Excellent communication skills and biomedical domain knowledge.
- Experience with de novo design, NGS data, Bayesian optimization, and antibody biology is a plus.
Benefits
- Salary estimated at $254,000 - $290,000 + bonus + options + benefits.
- Range of health insurance plan options.
- Dental and vision coverage.
- Additional well-being benefits.
- 401(k) with company match.
- DTO, two weeks of company-wide shutdown, and 12 company holidays.
- Paid parental leave.