Develop generative and predictive machine learning models for antibody sequence, structure, and property design. Build multi-objective protein optimization and LLM-driven methods for lab-in-the-loop drug development, deploy models with engineering teams, and support therapeutic programs. Collaborate across machine learning, drug development, wet-lab science, automation, and data science teams to accelerate the creation of safer, more effective biologic therapies.
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.
Key Responsibilities
- 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.
Skills Knowledge and Expertise
- 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.
Total Rewards
The salary estimated for this position is $150,000 - $200,000 + bonus + options + benefits. Compensation will vary depending on job-related knowledge, skills, and experience. Actual compensation will be confirmed in writing at the time of the offer.
About
BigHat Biosciences designs safer, more effective biologic therapies for patients using machine learning and synthetic biology. BigHat integrates a wet lab for high-speed characterization with machine learning technologies to guide the search for better antibodies. We apply these design capabilities to develop new generations of safer and more effective treatments for patients suffering from today’s most challenging diseases.BigHat is a Series B biotech outside San Francisco with a team-oriented, inclusive, and family-friendly culture. Our broad pipeline of wholly-owned and partnered therapeutic programs span many disparate indications with high unmet need, such as cancer, inflammation, and infectious disease. BigHat has raised >$100M from top investors, including Section 32, a16z, and 8VC.
BigHat Biosciences San Mateo, California, USA Office
1900 Alameda de Las Pulgas, San Mateo, CA, United States, 94403
Similar Jobs
Fintech
Develop and productionize data and AI solutions across the full machine learning lifecycle, including problem identification, model design, training, deployment, monitoring, and impact measurement. Partner with Operations, Engineering, Product, Fraud, Compliance, and Credit teams to manage risk and improve customer experience. Apply statistical techniques, build APIs, integrate ML systems into production services, and collaborate with technical and non-technical stakeholders.
Top Skills:
APIsMachine Learning FrameworksPythonSQL
Artificial Intelligence • Biotech
Lead the design and deployment of large-scale bioinformatics and omics pipelines spanning genomics, transcriptomics, and proteomics. Integrate multimodal biological data with machine learning, develop reproducible analysis frameworks, benchmark predictive models, and collaborate with wet-lab scientists to guide molecular engineering and experimental campaigns. Contribute novel computational biology algorithms, research roadmaps, and scientific leadership.
Top Skills:
DagsterGenomicsJaxMachine LearningNextflowProteomicsPythonPyTorchRSingle-Cell OmicsTranscriptomics
Artificial Intelligence • Healthtech • Biotech • Pharmaceutical
Develop and optimize machine learning methods for intrinsically disordered proteins, including generative models for protein conformational ensembles, affinity prediction, and generative chemistry. Build scalable training pipelines, adapt research methods, and collaborate with computational and experimental scientists to validate models using biophysical data.
Top Skills:
Computational BiologyGenerative ChemistryGenerative ModelsMachine LearningMolecular SimulationProtein ModelingStructural Modeling
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
