ML Scientist: Generative Antibody Design for Drugs

BigHat Biosciences; San Mateo (CA); On-site; USD 150,000 - 200,000; Full time

Benefits offered by this job

Bonus

Stock options

Benefits

Job summary

BigHat Biosciences in San Mateo, CA is seeking a Machine Learning Scientist or Engineer to advance ML-driven therapeutic antibody design. You will work on state-of-the-art generative models and predictive models trained on large proprietary datasets, integrating with a high-throughput wet-lab and LIMS infrastructure.

The role emphasizes hands-on methods development, deployment, and collaboration with interdisciplinary teams to accelerate design of new drugs.

Qualifications

• PhD in ML/CS/EE or related field with hands-on ML method development.
• Strong Python and PyTorch experience with software engineering practices.
• Excellent communication and ability to work with diverse teams.
• Ambitious and able to work in a fast-paced environment.
• Familiarity with ML-driven protein engineering is preferred.
• Nice-to-haves: de novo design, NGS data, Bayesian optimization, AWS experience.

Responsibilities

• Design and implement next state-of-the-art generative models for antibody sequences and structures.
• Develop multi-modality, multi-objective protein sequence optimization for lab-in-the-loop design.
• Develop and deploy agentic and LLM-driven optimization methods to accelerate design-build-test loop.
• Provide ML expertise to ongoing therapeutics programs and drug development.
• Collaborate with engineering to deploy models and methods efficiently.
• Work with interdisciplinary teams across drug developers and wet labs.

Skills

Python

PyTorch

Software engineering

Biomedical knowledge

Communication skills

CI/CD

Education

PhD in ML/CS/EE

Tools

AWS

CI/CD

Job description

BigHat Biosciences in San Mateo, CA is seeking a Machine Learning Scientist or Engineer to advance ML-driven therapeutic antibody design. You will work on state-of-the-art generative models and predictive models trained on large proprietary datasets, integrating with a high-throughput wet-lab and LIMS infrastructure.

The role emphasizes hands-on methods development, deployment, and collaboration with interdisciplinary teams to accelerate design of new drugs.