Machine Learning Scientist I/II, Decision Making for Physical Sciences

Lila Sciences • Cambridge, MA

20h • $176,000 - $304,000

About The Position

As a Machine Learning Scientist focused on decision-making, you will design, implement, and collaboratively productionize algorithms that determine a sequence of experimental choices within Lila SSI’s toolbox. Your work will maximize the impact of data acquisition under real-world constraints by integrating uncertainty-aware models with practical data collection strategies, accelerating discovery cycles in materials science and broader physical science domains.

Requirements

• Advanced degree (PhD or MS with equivalent research/industry experience) in Computer Science, Applied Math/Statistics, Physics, Materials Science, Chemical Engineering, or related field.
• Strong foundation in sequential decision‑making: Bayesian Optimization, active learning, contextual bandits, model-based RL, or Bayesian experimental design.
• Proficiency in Python and modern ML tooling (e.g., PyTorch/JAX; BoTorch/GPyTorch/Ax or similar); strong software engineering practices.

Nice To Haves

• Background in materials/chemistry or physical‑science experimentation, including autonomous/closed‑loop workflows.
• Familiarity with scientific simulation (e.g., DFT/MD) and integrating surrogate models with simulators.
• Open‑source contributions or publications in BO/RL/active learning.

Responsibilities

• Bayesian Optimization pipelines with acquisition functions tailored for diverse, real-world scientific settings (e.g., BoTorch/Ax stacks).
• Episodic reinforcement learning policies for multi‑step planning, including safe exploration, early stopping, and budget‑aware strategies (e.g., model-free and model‑based RL, contextual bandits).
• Multi‑fidelity and active‑learning workflows that combine diverse, noisy data sources and adaptive sampling methods with real-world constraints.
• Robust uncertainty quantification and calibration for scientific decision‑making.
• Reliable, reproducible code and services that scale from offline benchmarking to online, real-world deployment.
• Communicate findings succinctly to scientific, engineering, and leadership audiences; publish or present impactful results when appropriate.

Benefits

• We expect the base salary for this role to fall between $176,000- $304,000 USD per year, along with bonus potential and generous early equity.

What This Job Offers

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