ML Research Scientist - Computational Biophysics

Merge Labs is a frontier research lab with the mission of bridging biological and artificial intelligence to maximize human ability, agency and experience. We’re pursuing this goal by developing fundamentally new approaches to brain-computer interfaces that interact with the brain at high bandwidth, integrate with advanced AI, and are ultimately safe and accessible for anyone to use.

About the team:

Our Bio team designs, builds, and characterizes the biotechnologies that form the foundation of next-generation brain-computer interfaces. We combine molecular engineering, synthetic biology, neuroscience and advanced physical methods such as ultrasound to establish less invasive, high-bandwidth connections with neurons. The Bio team develops our core molecular technologies, validates their performance in vitro and in vivo, and demonstrates their advanced capabilities in animal models. We build custom experimental setups and pipelines and collaborate closely with engineers and data scientists. We work across disciplines to come up with creative ideas and solve some of the most challenging problems in biotechnology.

About the role:

We’re hiring a Senior / Principal ML Biophysicist to lead the development of scalable molecular dynamics pipelines and integrate physics-based models with machine learning frameworks. Starting from first principles, you’ll architect the company’s molecular modeling foundations—establishing tools and workflows for simulating, analyzing, and interpreting biomolecular dynamics to function relationships. Over time, you’ll help translate these into predictive frameworks that accelerate molecular engineering, inform experimental campaigns, and enable the discovery of highly functional molecules.

In this role, you will:

• Build the scientific and engineering scaffolding for protein structure modeling, molecular dynamics, and integrations with downstream ML frameworks.

• Collaborate with wet-lab scientists to define tractable optimization objectives and encode domain specific priors and constraints.

• Prototype modeling frameworks using internal and public datasets; benchmark and validate performance.

• Serve to non-domain experts for democratization of first-principles analysis

• Drive the development of ML frameworks that explicitly incorporate first-principles priors.

• Stay up-to-date with the latest research in deep-learning, molecular dynamics, and protein structure modeling 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.

You might thrive in this role if you have:

• Strong grounding in deep-learning, protein-structure modeling, and molecular dynamics.

• 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 haves

• Familiarity with neuroscience.

• Familiarity with language / state-space models.

If you're excited about this role but don't meet every qualification, please apply. As we build, we're hiring for complementary strengths to form a high-impact team.
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Merge Labs does not discriminate on the basis of race, color, religion, national origin, age, sex, sexual orientation, gender, gender identity, gender expression, marital status, physical or mental disability, medical condition, genetic information, family status, ancestry, citizenship, U.S. military (state and federal) and veteran status, or any other legally protected status. It is our intention that all applicants be given equal opportunity and that selection decisions are based on job related factors. We are an equal opportunity employer.

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We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing [email protected].