Director of Machine Learning

BigHat Biosciences•San Mateo, CA

7d•$250,000 - $365,000

About The Position

BigHat Biosciences is seeking a player-coach to lead the technical direction and hands-on execution of its machine learning capabilities and machine learning operations, including agent development and the use of agents to automate and extend its computational platform. Reporting to the CIO, the Director will shape ML-driven antibody design and lead development of antibody property models that iteratively improve designs, with proof points demonstrating their impact. The role also includes building agent services, working with frontier AI labs to adapt LLMs to agentic antibody design, and advancing modeling capabilities for therapeutic programs and external partners. The Director will lead machine learning within a production platform that combines computational capabilities with an automated, high-throughput wet lab. The work is focused on synthesizing real antibodies with drug-like properties and improving patient outcomes. The role will guide strategy, deliver models and computational results for internal and partner programs, develop platform offerings, lead a small senior team, and represent BigHat in the ML and protein engineering communities.

Requirements

Ph.D. in Machine Learning, Computer Science, Computational Biology, or a related field.
At least 8 years of experience developing and applying advanced machine learning methods.
Demonstrated leadership of technical teams or major technical programs.
Deep, current command of modern machine learning, including generative models of protein sequence and structure, Bayesian optimization, active learning, uncertainty quantification, and data-efficient predictive modeling or in-context learning.
Strong Python and PyTorch skills, with comfort in modern software engineering practices, cloud computing, and MLOps.
Experience shipping machine learning into production systems, with attention to reproducibility, provenance, benchmarking, and cycle time.
Ability to reason from first principles, design falsifiable experiments, communicate technical ideas clearly to interdisciplinary scientists, and support views on the direction of protein ML with a track record.
Player-coach approach, including willingness to write code and run benchmarks alongside the team.
Excellent written and verbal communication and ability to collaborate across protein science, automation engineering, software engineering, and drug development.
Motivation to advance BigHat’s mission to bring safer, more effective antibody therapies to patients.

Nice To Haves

Industry experience developing and applying advanced machine learning methods.
Working knowledge of antibody biology, developability, and drug development, or demonstrated ability and appetite to acquire this knowledge quickly.
Familiarity with display, next-generation sequencing (NGS), and high-throughput biophysical data.
Credibility with partners and external audiences.

Responsibilities

Own and continuously refine BigHat’s machine learning roadmap, articulating where the field is going and where the company intends to lead.
Use BigHat’s data repository and data-generation capabilities to develop multi-objective predictive models and demonstrate that better models accelerate therapeutic development.
Develop and train models supporting the safe and effective creation of increasingly sophisticated multispecific antibodies.
Apply frontier models to protein design challenges using BigHat’s datasets and data capabilities, and optimize the design loop by integrating the lab, computational platform, data, and LLM automation.
Coordinate across the CIO organization to guide a unified LLM and ML approach, and drive adoption of safe, impactful LLMs, agent frameworks, and reinforcement-learning evaluation and benchmarking frameworks.
Ensure models, provenance, and benchmarks flow through infrastructure reliably, and that build-test-train design rounds use model insights across the more than 2,000 antibodies designed each week.
Deliver computational analyses, predictions, and optimized variant sets for internal and partnered programs to support progress toward clinic-ready molecules.
Work with business development, partnerships, and executive leadership to develop purchasable offerings from data-generation and modeling capabilities, scope pilots, define technical success, and deliver results.
Mentor and grow a small senior team of ML scientists and engineers, setting standards for rigor, collaboration, and intellectual honesty.
Publish, present, and engage with the machine learning and protein engineering communities to strengthen BigHat’s external reputation.