Machine Learning Engineer

Bighat Biosciences - San Mateo, CA

Full-time - Mid Level

San Mateo, CA

About the position

The Machine Learning Engineer at BigHat Biosciences will play a crucial role in developing, deploying, and refining tools that accelerate the antibody design cycle. This position is ideal for a self-motivated and experienced individual who is passionate about applying machine learning to address unmet patient needs. The engineer will work within a tightly integrated machine learning stack that collaborates with a high-throughput wet lab, enabling rapid design, validation, and iterative refinement of ML-engineered antibody therapeutics. The bespoke platform at BigHat channels data from automated lab instruments through various stages, including data processing pipelines, custom validation and quality control, ML data ingestion ETLs, and predictive ML models. The ultimate goal is to produce optimized antibody designs that are closer to beneficial therapeutics while generating valuable training data along the way. In this role, the Machine Learning Engineer will not only focus on model development but will also engage in a collaborative environment with wet lab scientists to validate tools in a weekly build-test-train loop. The emphasis is on trust in algorithms that have successfully designed real antibodies with drug-like properties. The engineer will provide ML expertise and support for ongoing therapeutic programs, contributing directly to the development of new drugs. Additionally, collaboration with the engineering team is essential to design, develop, and deploy production-grade infrastructure that enhances automated model training, tracking, benchmarking, and lab validation across diverse tasks in a continuous data generation setting. The position requires working closely with an interdisciplinary team, identifying inefficiencies, and implementing solutions to improve the BigHat ML platform.

Responsibilities

• Develop, source, and deploy generative models of antibody sequence and structure.
• Create predictive models of antibody properties and lab-in-the-loop optimization algorithms.
• Collaborate with wet lab scientists to validate tools in a weekly build-test-train loop.
• Provide ML expertise and support for ongoing therapeutics programs.
• Contribute to the development of new drugs through machine learning applications.
• Work with the engineering team to design and deploy production-grade infrastructure for model training and validation.
• Identify inefficiencies in the ML platform and implement solutions.

Requirements

• BS/MS in a relevant field.
• 5+ years of hands-on experience crafting, refining, and deploying ML models.
• Strong competency in Python and familiarity with PyTorch.
• Experience deploying ML and data science stacks on AWS.
• Knowledge of relational databases and RESTful APIs.
• Excellent communication skills and sufficient biomedical domain knowledge.

Nice-to-haves

• Experience with protein structure modeling and biophysics.
• Familiarity with NGS data.
• Knowledge of antibody biology and biophysics.