LILA has created the world's first Operating System for Science powered by Scientific Superintelligence™. Backed by General Catalyst.
About the role
Lila Sciences builds AI systems that accelerate discovery across the physical and life sciences. Within Physical Sciences AI, we work with experimental teams to build autonomous discovery agents that decide which experiments Lila's AI Science Factories should run next. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Pursuing a Master's or PhD in statistics, ML, CS, operations research, applied math, or a quantitative science
- Solid grounding in Bayesian inference and decision making, including priors and posterior predictive distributions
- Research experience in Gaussian processes, Bayesian optimization, bandits, reinforcement learning, or probabilistic modeling
- Strong Python skills with PyTorch and probabilistic libraries such as Pyro, PyMC, or BoTorch
- Comfort working with small, noisy, and heterogeneous datasets
- Clear communication with both ML and experimental collaborators
More about this role
Lila Sciences builds AI systems that accelerate discovery across the physical and life sciences. Within Physical Sciences AI, we work with experimental teams to build autonomous discovery agents that decide which experiments Lila's AI Science Factories should run next.
Real scientific campaigns rarely look like textbook optimization problems. A single campaign can mix large discrete choices (which components, which recipe) with continuous settings (amounts, temperatures, times), pursue several objectives under hard constraints, and draw on evidence from literature and characterization data. Every experiment costs real time and materials. Off-the-shelf methods often struggle here, and they ignore the domain structure that could guide the search.
As a Bayesian Methods Co-Op, you will work alongside research scientists and engineers on a focused project within one broad question: how AI systems can make better experimental decisions. Depending on your background, that could mean building agents that choose the next experiment, testing when in-context learners can be trusted as decision-makers, or developing Bayesian strategies for tight experimental budgets. Your methods will be...
Browse similar: AI jobs · Startup internships · AI startup jobs · Startup jobs · San Francisco Bay Area