Senior Machine Learning Scientist, Model-Driven Protein Optimization
Clinical-stage generative-biology company founded in 2018, developing protein-based medicines with machine learning
| Employer | Clinical-stage generative-biology company founded in 2018, developing protein-based medicines with machine learning (not named) |
|---|---|
| Location | Somerville, MA (Boston) · Hybrid |
| Type & level | Full-time · Senior |
| Pay | USD 192k–265k base Source: pay range stated in the source posting |
| Visa path | O-1A fit |
| Role family | Protein Design & Structural Biology Scientist |
About the role
This role sits in a team that builds the machine learning methods and closed-loop systems deciding which therapeutic proteins to design, build and test next. The scientist works on sequential decision-making, experimental design and property modeling for a lab-in-the-loop optimization platform. It is both a technical leadership and a hands-on position.
What you'll do
- Develop machine learning methods and systems for lab-in-the-loop protein optimization, including property models and multi-objective strategies.
- Shape data-generation strategies so experimental campaigns are as informative as possible for model improvement.
- Build LLM-enabled and agentic workflows that help scientists explore design hypotheses and connect models to data and experiments.
- Write and maintain production-quality models, software and data workflows that are reliable and reproducible.
- Work with ML engineering and software teams to integrate components into scalable platform capabilities.
- Partner with protein designers and wet-lab scientists, and help set technical direction by proposing projects and defining milestones.
Requirements
- PhD in machine learning, computational biology, computer science, applied mathematics, engineering or a related quantitative field.
- Practical experience with probabilistic machine learning, Bayesian optimization, active learning or experimental design.
- Experience applying ML to biological or experimental data, including noisy assays, sparse labels and bias.
- Strong Python skills and experience with frameworks such as PyTorch or JAX.
- Systems thinking and the ability to design technical interfaces with engineering partners.
- Clear communication across ML, engineering, protein design and experimental teams.
Nice to have
- Experience in protein design, antibody engineering, biologics discovery or drug development.
- Experience with design-build-test-learn cycles, high-throughput screening or directed evolution.
- Experience with multi-objective optimization, uncertainty calibration or library design.
- Experience with deep learning, including transformer architectures.
- Experience building LLM agents or contributing to shared ML platforms and tooling.
Pay & benefits
USD 192k–265k base. The posting lists eligibility for an annual bonus, equity compensation and a competitive benefits package.
Pay source: pay range stated in the source posting. Market data, not an offer from O1DMatch.
Visa path
Machine learning scientists in biotech often support an O-1A case with publications, citations, open-source contributions, patents, conference or reviewing roles and evidence of a critical role in high-impact work. The posting states no citizenship restriction. An employer like this or a U.S. agent can file the petition, and the outcome depends on the individual's evidence.
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