Your Impact at Lila

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.

What You'll Be Building

• 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.

What You’ll Need to Succeed

• 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.

Bonus Points For

• 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.

About Lila

Lila Sciences is the world’s first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science. We are pioneering a new age of boundless discovery by building the capabilities to apply AI to every aspect of the scientific method. We are introducing scientific superintelligence to solve humankind's greatest challenges, enabling scientists to bring forth solutions in human health, climate, and sustainability at a pace and scale never experienced before. Learn more about this mission at www.lila.ai

If this sounds like an environment you’d love to work in, even if you only have some of the experience listed below, we encourage you to apply.

Compensation

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. The final offer will reflect your unique background, expertise, and impact.

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.

Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy.

A Note to Agencies

Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.