Bayesian AI for Experimental Design Co-op

Lila Sciences; Cambridge (MA); On-site; USD 55,000 - 83,000; Part time

Job summary

Lila Sciences is seeking a Bayesian Methods Co-Op to collaborate with research scientists and engineers on optimizing experimental decisions using Bayesian strategies. You will develop autonomous agents that select experiments and evaluate models on live campaigns, potentially contributing to peer-reviewed work.

Ideal candidates are pursuing a Master’s or PhD in a quantitative field with strong Python skills and experience in Bayesian methods, Gaussian processes, and probabilistic modeling.

Qualifications

• 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.

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, 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.

Skills

Bayesian inference

Gaussian processes

Reinforcement learning

Probabilistic modeling

Communication

Education

Master's or PhD in statistics, ML, CS, OR, applied math, or quantitative science

Tools

PyTorch

Pyro

PyMC

BoTorch

Job description

Lila Sciences is seeking a Bayesian Methods Co-Op to collaborate with research scientists and engineers on optimizing experimental decisions using Bayesian strategies. You will develop autonomous agents that select experiments and evaluate models on live campaigns, potentially contributing to peer-reviewed work.

Ideal candidates are pursuing a Master’s or PhD in a quantitative field with strong Python skills and experience in Bayesian methods, Gaussian processes, and probabilistic modeling.