
BigHat Biosciences is a Series B biotech company based outside San Francisco, focused on developing safer and more effective antibody therapies using machine learning and synthetic biology. Their Milliner platform integrates a high-speed wet lab with advanced machine learning technologies for antibody discovery and engineering. With over $100 million raised from top investors, BigHat is positioned to tackle complex therapeutic challenges and has a pipeline of both wholly-owned and partnered programs.

BigHat Biosciences is a Series B biotech company based outside San Francisco, focused on developing safer and more effective antibody therapies using machine learning and synthetic biology. Their Milliner platform integrates a high-speed wet lab with advanced machine learning technologies for antibody discovery and engineering. With over $100 million raised from top investors, BigHat is positioned to tackle complex therapeutic challenges and has a pipeline of both wholly-owned and partnered programs.
Founded: 2019
Headquarters: San Mateo / Bay Area, California
Stage: Series B
Total funding: $75M Series B; ~$100–105M total reported
Platform: Milliner — ML-guided antibody design integrated with a high-throughput wet lab
Antibody discovery and engineering for therapeutic applications (oncology, inflammation, infectious disease).
2019
Biotechnology Research
$19,000,000
Announced as an oversubscribed Series A
$75,000,000
Participants included Amgen Ventures, Bristol Myers Squibb, Quadrille Capital, Gaingels, GRIDS Capital
“Presence of strategic corporate and top-tier VC investors (Section 32, Andreessen Horowitz, Amgen Ventures, Bristol Myers Squibb, 8VC) and Series B financing”
| Company |
|---|
Department: DS/ML (Data Science/Machine Learning)
Location: San Mateo, CA
Description The Role: We are seeking talented, hard working associates to join our Machine Learning team for a fixed-term role.
At BigHat Biosciences, we’ve re-framed antibody drug development as an iterative, machine learning–driven, multi-objective optimization problem. Our roboticized high-throughput wet-lab continually adds to our large proprietary datasets, which are piped through a custom 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.
As an ML Research Fellow you’ll work on developing novel ML models as well as helping with routine ML support of our ongoing therapeutics programs. Applications include multi-modal models of antibody biophysical properties, de novo and structure driven protein design, better protein language models, and active learning and bayesian optimization methods for embedding these models in our design-build-test loop, amongst many others. You’ll be mentored by an experienced ML scientist from our team and work closely with an interdisciplinary team of engineers, wet-lab scientists and drug developers to ensure your work is relevant for active drug development programs.
Key Responsibilities
Skills Knowledge And Expertise