Founding Machine Learning Scientist
Founding Machine Learning Scientist
A stealth-stage biotech venture is building the core technology to power a new generation of protein therapeutics.
The team combines deep expertise in cutting-edge AI research and generative modeling, including contributors to foundational work in probabilistic methods and large-scale model training.
Backed by leading investors, including a Midas List venture capitalist, as well as founders, early engineers, and executives from Meta and Google. The team is guided by globally recognized AI and computational science experts, this is a chance to work alongside people who have shaped the state of the art on problems at the intersection of machine learning and biology. The company combines generative machine learning with high-throughput biological experimentation to design programmable proteins that act precisely in disease-relevant contexts. This is an opportunity to join as one of the first 10 employees and shape the technical foundation of a company aiming to redefine drug discovery.
Key Responsibilities
The Founding Machine Learning Scientist will own core ML components from research to production, working closely with a cross-disciplinary team of AI researchers and drug discovery scientists.
Design, build, and deploy generative protein design models.
Implement closed-loop training paradigms integrated with high-throughput wet-lab experimentation.
Develop and scale training and inference pipelines across thousands of GPUs using Ray, Anyscale, and cloud platforms (AWS, GCP).
Establish rigorous validation and benchmarking protocols.
Ideal Profile
Background in computer science, applied math, computational biology, or related quantitative field.
2+ years building and running generative models.
Strong problem-solving skills and ability to work across disciplines.
Humble and eager to help colleagues. You do what it takes to help the team succeed.
Bonus: Experience with industrial-scale models (LLMs, diffusion models, flow matching) and peer-reviewed ML publications and/or meaningful contributions to open-source ML libraries.
If you're interested in joining a deep-tech team at the intersection of AI and biology and want to work on first-of-its-kind generative models for protein therapeutics, please apply.
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