About the job
At Merge Labs, we are at the forefront of research, dedicated to uniting biological and artificial intelligence to enhance human capability, autonomy, and overall experience. Our innovative approach focuses on developing revolutionary brain-computer interfaces that offer high-bandwidth interaction with the brain, seamlessly integrate advanced AI, and are designed to be safe and accessible for everyone.
About the Team:
Our Bio team is responsible for designing, constructing, and characterizing the biotechnologies that underpin the next generation of brain-computer interfaces. By integrating molecular engineering, synthetic biology, neuroscience, and cutting-edge physical methods such as ultrasound, we aim to establish less invasive, high-bandwidth connections with neurons. The Bio team is dedicated to developing our core molecular technologies, validating their performance both in vitro and in vivo, and showcasing their advanced capabilities in animal models. We create custom experimental setups and pipelines while collaborating closely with engineers and data scientists to tackle some of the most challenging problems in biotechnology.
About the Role:
We are seeking a Senior/Principal Machine Learning Biophysicist to spearhead the creation of scalable molecular dynamics pipelines, integrating physics-based models with machine learning frameworks. You will build the molecular modeling foundations of the company from first principles, establishing tools and workflows for simulating, analyzing, and interpreting biomolecular dynamics to elucidate function relationships. Over time, your contributions will help translate these frameworks into predictive models that expedite molecular engineering, guide experimental campaigns, and facilitate the discovery of highly functional molecules.
Key Responsibilities:
Develop the scientific and engineering framework for protein structure modeling and molecular dynamics, along with integrations into downstream ML frameworks.
Collaborate with wet-lab scientists to establish realistic optimization objectives and encode domain-specific priors and constraints.
Prototype modeling frameworks utilizing internal and public datasets; benchmark and validate performance.
Make complex analyses accessible to non-domain experts through democratization of first-principles analysis.
Lead the development of ML frameworks that explicitly incorporate first-principles priors.
Stay abreast of the latest advancements in deep learning and molecular dynamics.
