Machine Learning Scientist, BigHat Biosciences
$150-200k
+ Bonus and options
Office located in San Mateo, CA
Antibody discovery & engineering platform
Be an early applicant
Antibody discovery & engineering platform
21-100 employees
Be an early applicant
$150-200k
+ Bonus and options
Office located in San Mateo, CA
21-100 employees
Company mission
To improve human health by making it far easier to design advanced, next-generation antibody therapeutics.
Role
Who you are
- 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
What the job involves
- 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
Our take
Melding genetics and code, BigHat Biosciences is a protein therapeutics company that develops antibodies for the medical industry using machine learning and AI. Established in 2019, the company combines advanced computational models with an automated wet lab to design and test antibody therapies more quickly than traditional methods. Its technology has been applied to a range of use cases too, including efforts during the pandemic to accelerate vaccine and therapeutic development.
The platform has particular value in helping develop treatment for complex and hard-to-treat diseases such as Alzheimer's disease, where designing effective biologic drugs is especially challenging. To support this work, BigHat has partnered with major pharmaceutical companies including Merck & Co, and Amgen to apply its AI-driven antibody design platform to drug discovery and development.
More recently, BigHat has continued to expand its partnerships and pipeline as interest in AI-enabled drug discovery grows. The company has also collaborated with Eli Lilly and Company to help develop next-generation antibody therapeutics using its Milliner platform, while also advancing its own internal programmes in areas such as oncology. With candidates including a gastrointestinal cancer and antibody-drug conjugate expected to enter clinical trials in 2026, BigHat is positioning itself not only as a technology partner for pharmaceutical companies but as as developer of its own next-generation biologic medicines.
Kirsty
Company Specialist at Welcome to the Jungle
Company
Funding (last 2 of 3 rounds)
Jul 2022
$75m
SERIES B
Feb 2021
$19m
SERIES A
Total funding: $99.3m
Company benefits
- Culture of excellence and growth
- Swift promotions with clear standards
- Autonomy and responsibility
- Fair compensation and recognition
- True work life balance
Company HQ
Beresford Park, San Mateo, CA
Leadership
Peyton Greenside
(Co-Founder, CEO & President)Peyton originally served as the CSO at BigHat before moving into the role of CEO in 2025. Previously, she was a Founding Scientific Advisor at VALIS.
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