BigHat Biosciences

ML-guided antibody discovery and engineering

Machine Learning Scientist

Applied Machine LearningConfirmed live in the last 24 hours

Full-timeSan Mateo, CA, USANo salary listed

About the Job

Requirements

A PhD in Machine Learning, Computer Science, Electrical Engineering, or a relevant scientific discipline.
Hands-on experience developing and applying novel machine learning methods, along with a strong quantitative background.
Strong competency in Python.
Familiarity with PyTorch.
Experience with modern software engineering best practices, including agentic and large language model-assisted coding, testing, and continuous integration and continuous delivery.
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, as well as predictive models of antibody properties, trained on proprietary internal antibody datasets.
Develop multi-modality, multi-objective iterative protein sequence optimization approaches for lab-in-the-loop antibody design problems, and validate and deploy them in a high-throughput wet lab.
Develop, refine, and deploy agentic and large-language-model-driven optimization methods to automate and accelerate the design-build-test loop.
Provide machine learning expertise and support for ongoing therapeutics programs, contributing directly to the development of new drugs.
Collaborate with the engineering team to maximize the efficiency of automated deployment for the latest models and methods.
Work with an interdisciplinary team of drug developers, wet-lab scientists, automation specialists, and data scientists.

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.
Publications at major machine learning conferences.

The role: We are seeking a creative, ambitious Machine Learning Scientist or Engineer to advance the state of the art in ML-driven therapeutic antibody design.

At BigHat Biosciences our full-stack antibody drug development platform uses AI/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 development of complex, net-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 need, and a curiosity about the underlying biology, you’ll apply your top-tier ML skillset to refine and expand this state of the art protein engineering platform. Success will mean not only hands-on methods development, but actively participating in the application of our platform to the accelerated design of new drugs for devastating diseases.

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.
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.
Develop, refine, and deploy agentic and LLM-driven optimization methods to further automate and accelerate our design-build-test loop.
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 deployment of our latest models and methods.
Work closely with an interdisciplinary team of drug developers, wet lab scientists, automation specialists, data scientists, etc. - every therapeutics program at BigHat is heavily interdisciplinary.

PhD in ML/CS/EE or relevant scientific discipline, hands on experience developing and applying novel ML methods and a strong quantitative background.
Strong competency in Python, familiarity with PyTorch (even without LLMs!) and experience with modern software engineering best practices, including not just agentic/LLM-assisted coding but testing, CI/CD, etc.
Excellent communication skills, sufficient biomedical domain knowledge to interact effectively with diverse scientific teams.
Energy and ambition - ready to dive into a fast-paced environment and execute 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, experience training and deploying models on AWS, and publications at major ML conferences.

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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