Your Impact at LILA

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 tested against live campaigns.

What You'll Be Building

Design and benchmark experiment-selection strategies that balance exploration and exploitation under realistic budgets
Build autonomous discovery agents that propose the next experiment from surrogate models
Develop in-context learning approaches inspired by Bayesian inference, evaluating calibration and robustness
Run retrospective and live evaluations on active campaign data with clear baselines and metrics
Present findings to ML and experimental teams, with the option to contribute to a publication

What You'll Need to Succeed

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

Bonus Points For

Applying these methods to real problems in materials science, chemistry, or another physical science
Familiarity with in-context learning as implicit Bayesian inference, meta-learning, or prior-data fitted networks
Optimization over categorical or combinatorial spaces, including mixed-variable, multi-objective, or batch settings
Reinforcement learning for sequential decision making or Bayesian experimental design
Training models on multimodal scientific data such as text, images, spectra, or simulations

About LILA

Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.

LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.

Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.

We’re All In

Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.

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