ML Research Scientist - De Novo Design
San Francisco Bay Area$200k - $270kFull-timePosted 9mo ago
About the role
Senior/Principal ML Scientist to design and scale de novo design frameworks guiding molecular engineering campaigns through iterative DBTL cycles, building closed-loop optimization backbone and production pipelines to improve experimental throughput and discovery.
Responsibilities
- Build the scientific and engineering scaffolding in collaboration with data-engineering & MLOps for de novo design and closed-loop optimization, including data ingestion, ML modeling, and library design.
- Collaborate with wet-lab scientists to define tractable optimization objectives and encode domain specific priors and constraints.
- Prototype de novo design frameworks using internal and public datasets; benchmark and validate model performance.
- Integrate ML models with experimental data streams and serve to non-domain experts for model democratization.
- Extend ML frameworks to handle multi-objective or constrained optimization problems.
- Stay up-to-date with the latest research in de novo design, and prototype novel algorithms that can be deployed to improve the company’s discovery or development workflows.
- Contribute to the long-term research roadmap and serve as a thought-leader for scientists.
Qualifications
- Strong grounding in SSMs, LLMs, SE(3)-equivariance, Flow-matching.
- Working knowledge of transfer-learning strategies.
- Proficiency in Python / PyTorch / Jax and comfort writing clean, reproducible production grade code.
- Experience bridging machine learning and experimental science – working with sparse, noisy, and or high-cost data.
- A collaborative, systems-level mindset.
- Nice to have: familiarity with neuroscience.
Skills
Required:Ssm, LLMs, SE(3)-equivariance, Flow-matchingTransfer-learningPython / PyTorch / Jax
Last updated Jul 22, 2026. Interested? Apply on the company site.
About Merge Labs
Introducing Merge Labs
Meet Merge Labs – a research lab with the long-term mission of bridging biological and artificial intelligence to maximize human ability, agency, and experience.
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