Lila Sciences · 5 hours ago
Machine Learning Scientist I/II, Decision Making for Physical Sciences
Cambridge, MA
Full-time
Hybrid
Entry, Mid Level
$176K/yr - $304K/yrLila Sciences is pioneering a new age of boundless discovery by building a scientific superintelligence platform for life, chemistry, and materials science. They are seeking a Machine Learning Scientist who will design and implement algorithms for decision-making in experimental choices, focusing on maximizing data acquisition and accelerating discovery cycles in materials science and physical sciences.
Artificial Intelligence (AI)Foundational AILife ScienceSoftware
Responsibilities
Design, implement, and collaboratively productionize algorithms that determine a sequence of experimental choices
Maximize the impact of data acquisition under real-world constraints by integrating uncertainty-aware models with practical data collection strategies
Build Bayesian Optimization pipelines with acquisition functions tailored for diverse, real-world scientific settings
Develop episodic reinforcement learning policies for multi‑step planning, including safe exploration, early stopping, and budget‑aware strategies
Create multi‑fidelity and active‑learning workflows that combine diverse, noisy data sources and adaptive sampling methods with real‑world constraints
Ensure robust uncertainty quantification and calibration for scientific decision-making
Produce 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
Qualification
Required
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
Preferred
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
Benefits
Bonus potential
Generous early equity
Company
Lila Sciences
Lila Sciences creates a scientific superintelligence platform and autonomous labs for life sciences, chemistry, and materials science. It is a sub-organization of Flagship Pioneering.
Founded in 2023
201-500 employees
H1B Sponsorship
Lila Sciences has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
Represents job field similar to this job
Trends of Total Sponsorships
2025 (8)
Funding
Current Stage
Growth StageTotal Funding
$550.67MKey Investors
ARIANVenturesFlagship Pioneering
2026-01-20Grant· $0.67M
2025-10-14Series A· $115M
2025-09-14Series A· $235M
Recent News
MIT Technology Review
2026-01-22
2026-01-03
2025-12-17
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