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Machine Learning Engineer
Job Location:
Job Location:
San Mateo, CA - USA
Yearly Salary:
USD 150000 - 200000
Yearly Salary:
Posted:
29 September 2026 (20 hours ago)
Posted:
Application Deadline:
27 December 2026
Application Deadline:
Vacancies:
1 Vacancy
Vacancies:
Job Summary
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.
Youre 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 youll 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
- Masters in ML/CS/EE or Bachelors with 3 years industry experience; 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 $150000 - $200000 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.
Required Experience:
IC
About Company
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 desig ... View more
