De Novo Design ML Scientist for Brain-Computer Interfaces

Merge Labs; San Francisco (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

A frontier research lab in San Francisco is seeking a Senior/Principal ML Scientist to lead the design and scaling of frameworks guiding molecular engineering. This role includes building data and modeling foundations for experiments, integrating ML models, and contributing to research in de novo design. Ideal candidates have a strong background in machine learning, proficiency in Python / PyTorch, and experience in experimental science. Open to applicants with diverse qualifications, ensuring equal opportunity.

Qualifications

• Strong grounding in SSMs, LLMs, SE(3)-equivariance, Flow-matching.
• 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.

Responsibilities

• Build scientific and engineering scaffolding for de novo design and closed-loop optimization.
• Collaborate with wet-lab scientists to define optimization objectives.
• Prototype de novo design frameworks using datasets and benchmark model performance.
• Integrate ML models with experimental data streams.
• Stay up-to-date with the latest research in de novo design.

Skills

Strong grounding in SSMs

Working knowledge of transfer-learning strategies

Proficiency in Python

Experience bridging machine learning and experimental science

Collaborative mindset

Familiarity with neuroscience

Tools

PyTorch

Jax

Job description

A frontier research lab in San Francisco is seeking a Senior/Principal ML Scientist to lead the design and scaling of frameworks guiding molecular engineering. This role includes building data and modeling foundations for experiments, integrating ML models, and contributing to research in de novo design. Ideal candidates have a strong background in machine learning, proficiency in Python / PyTorch, and experience in experimental science. Open to applicants with diverse qualifications, ensuring equal opportunity.