LILA has created the world's first Operating System for Science powered by Scientific Superintelligence™. Backed by General Catalyst.
About the role
Lila is redefining the future of medicine by combining automated large-scale data generation with scientific superintelligence. At Lila, we are building the loop where AI, automation, and experimental biology co-evolve to solve the hardest problems in medicine.
What they're looking for
- PhD in machine learning, statistics, computational biology, computer science, bioengineering, physics, or a related quantitative field, with a strong publication record or equivalent industry impact
- Experience developing models for high-dimensional biological data, and the judgment to connect ML methods to biological mechanism and experimental design
- Uncertainty and evaluation rigor. Comfort with calibration, proper scoring rules, and evaluation design, in work where someone made a decision based on your numbers
- Strong programming skills, reliable ML research workflows, and clear communication across ML, biology, and experimental teams. Track record of leading ambiguous research problems from formulation through execution
- Communicate findings clearly to technical and cross-functional audiences, including scientists, engineers, product partners, and therapeutic stakeholders
- Support external scientific visibility through publications, presentations, and engagement with ML/AI for Biology, computational biology and therapeutic discovery communities, as appropriate
More about this role
Lila is redefining the future of medicine by combining automated large-scale data generation with scientific superintelligence. At Lila, we are building the loop where AI, automation, and experimental biology co-evolve to solve the hardest problems in medicine.
The Life Science AI team develops machine learning systems for automated reasoning on biological data, combining state-of-the-art ML with breakthrough biology. We seek a Senior ML Scientist to focus on domain models underneath that system: models of perturbation biology, genetics, and and high-dimensional experimental readouts that connect biological mechanism, experimental intervention and therapeutic opportunity.
At Lila, the data is generated for the model: we run our own experiments at scale, and you will help decide what gets measured. The question is not only what to learn from a fixed dataset, but what datasets should exist. And model outputs are reasoned over. Predictions go to systems and scientists choosing what to run next and making mechanistic calls downstream. Both need to know why, not only what. A model that returns a quantity a biologist, human or AI, can argue with; models that transfer to new contexts,...
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