Co-Op, Bayesian Methods for Complex Decision Making

Lila Sciences • Cambridge, MA

9d

About The Position

Lila Sciences is seeking a Bayesian Methods Co-Op to work with research scientists and engineers on how AI systems can make better experimental decisions within Physical Sciences AI. The team develops autonomous discovery agents that select experiments for AI Science Factories. Scientific campaigns may involve discrete and continuous choices, multiple objectives, hard constraints, evidence from literature and characterization data, and limited time and materials. The Co-Op will contribute to a focused project based on their background, with methods tested against live campaigns.

The work may involve building agents to choose the next experiment, evaluating whether in-context learners can be trusted as decision-makers, or developing Bayesian strategies for tight experimental budgets. The role includes designing and benchmarking experiment-selection methods, developing autonomous discovery agents and Bayesian-inspired in-context learning approaches, and evaluating methods on active campaign data. The Co-Op will present findings to machine learning and experimental teams, with an opportunity to contribute to a publication.

Requirements

• Pursuing a Master's or PhD in statistics, machine learning, computer science, operations research, applied mathematics, 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 machine learning and experimental collaborators.

Nice To Haves

• Experience 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.
• Experience with optimization over categorical or combinatorial spaces, including mixed-variable, multi-objective, or batch settings.
• Experience with reinforcement learning for sequential decision making or Bayesian experimental design.
• Experience training models on multimodal scientific data such as text, images, spectra, or simulations.

Responsibilities

• 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, and evaluate their calibration and robustness.
• Run retrospective and live evaluations on active campaign data using clear baselines and metrics.
• Present findings to machine learning and experimental teams.
• Optionally contribute to a publication.

What This Job Offers

• Job Type: Full-time
• Career Level: Intern