BigHat Biosciences

ML-guided antibody discovery and engineering

Principal Machine Learning Scientist

AI ResearchConfirmed live in the last 24 hours

Full-timeSan Mateo, CA, USANo salary listedRemote

About the Job

Requirements

PhD in Machine Learning, Computer Science, or the hard sciences, plus 5+ years of post-graduation experience developing and applying novel machine learning methods.
A strong quantitative background.
Publications in major machine learning conferences and/or leading journals, with an extensive demonstrable track record developing and applying novel machine learning in industry.
Strong competency in Python, familiarity with PyTorch, and experience with modern software engineering best practices.
Excellent communication skills and sufficient biomedical domain knowledge to interact effectively with diverse scientific teams.
Familiarity with the current state of the art in machine-learning-driven protein engineering.

Responsibilities

Design and implement generative models of antibody sequence and structure, and predictive models of antibody properties, using proprietary antibody datasets.
Provide leadership, technical guidance, and mentorship to other machine learning and data science full-time employees and interns.
Help set strategy for future machine learning research based on an understanding of company programs, operations, and real-world drug development challenges.
Develop, refine, and deploy de novo design methods for generating initial hits to challenging therapeutic targets.
Develop multi-modality, multi-objective iterative protein sequence optimization approaches for lab-in-the-loop antibody design, and validate and deploy them in a high-throughput wet lab.
Maintain an in-depth understanding of the state of the art in machine-learning-driven protein engineering.
Share findings at top-tier conferences and publish in leading scientific journals to advance protein engineering.
Provide machine learning expertise and support for ongoing therapeutics programs, contributing directly to new drug development.
Collaborate with the engineering team to deploy the latest models efficiently and in an agentic manner to therapeutics programs.
Work with interdisciplinary drug developers, wet-lab scientists, automation specialists, and data scientists to identify platform inefficiencies or improvements, and plan and prioritize machine-learning-methods development.

Desired Qualifications

Experience with de novo design.
Experience with next-generation sequencing data.
Experience with Bayesian optimization.
Familiarity with antibody biology and drug development.
Experience training and deploying models on Amazon Web Services.

The role: We are seeking a creative, accomplished 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 ML to drive every stage from discovery to optimization. Our roboticized high-throughput wet-lab continually adds to our large proprietary datasets, which are piped through a custom LIMS++ data management and orchestration layer to automatically update and deploy the latest models. This makes the development of complex, next-gen therapeutics ‘trivially parallelizable’, at a pace which only accelerates as we develop better ML tooling.

You’re not interested in just git-cloning the latest NeurIPS pub and swapping out the dataset. Motivated by an enthusiasm for the possibility of addressing unmet patient needs and a curiosity about the underlying biology, you’ll apply your world-class ML skillset to refine and expand this state-of-the-art protein engineering platform. Success will mean not only hands-on methods development, but helping shape the direction for future ML research, and actively participating in the application of our platform to the accelerated design of new therapeutics.

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.

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.

About the Company

BigHat Biosciences

ML-guided antibody discovery and engineering

BigHat Biosciences designs and develops antibody therapies by combining machine learning with synthetic biology. Its Milliner platform integrates a wet-lab workflow with AI to discover, engineer, and fully characterize hundreds of recombinant antibodies each week, enabling rapid development of safer and more effective treatments for hard-to-treat diseases such as certain infections and cancers. Unlike traditional antibody discovery, Milliner provides end-to-end screening, purification, and characterization in a high-throughput, weekly workflow, speeding up the path from concept to candidate. The company differentiates itself through its integrated platform that couples ML-driven design with synthetic biology experimentation, a focus on challenging diseases, and a culture that emphasizes work-life balance. Its goal is to deliver faster, safer antibody therapies to patients by expanding access to rapid discovery and engineering capabilities for healthcare providers and patients alike.

Company size: 51-200
Company stage: Series C
Total funding: $174.3M
Headquarters: San Mateo, CA
Founded: 2019

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