Machine Learning Scientist (AI)

TL;DR

Machine Learning Scientist (AI): Design, implement, and productionize algorithms that determine a sequence of experimental choices within Lila SSI’s toolbox with an accent on integrating uncertainty-aware models with practical data collection strategies. Focus on Bayesian Optimization, episodic reinforcement learning, and multi-fidelity workflows to accelerate discovery cycles in materials science and broader physical science domains.

Location: Cambridge, MA USA (onsite)

Salary: $176,000-$304,000 USD per year

Company

hirify.global is a pioneering scientific superintelligence platform and autonomous lab dedicated to applying AI to every aspect of the scientific method to solve humankind's greatest challenges in human health, climate, and sustainability.

What you will do

• Design, implement, and productionize Bayesian Optimization pipelines with tailored acquisition functions for scientific settings.
• Develop episodic reinforcement learning policies for multi-step planning, including safe exploration and budget-aware strategies.
• Create multi-fidelity and active-learning workflows that combine diverse, noisy data sources and adaptive sampling methods.
• Ensure robust uncertainty quantification and calibration for scientific decision-making.
• Develop reliable, reproducible code and services that scale from offline benchmarking to online deployment.
• Communicate findings succinctly to scientific, engineering, and leadership audiences, publishing impactful results when appropriate.

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 have

• 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.

Culture & Benefits

• Competitive base salary with bonus potential and generous early equity.
• Commitment 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.
• Opportunity to work on solutions for human health, climate, and sustainability at an unprecedented pace and scale.