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Periodic Labs

AI-driven materials discovery and design

Research Scientist - Scaling RL

AI ResearchConfirmed live in the last 24 hours
Full-timeIn-person

About the Job

Requirements

  • Hands-on experience training large language models with reinforcement learning.
  • Bring a rigorous, detail-oriented scientific approach to answering research questions.
  • Be able to devise small-scale reinforcement-learning setups that transfer to large-scale training runs.
  • Be comfortable working across a complex training stack to implement, debug, and test new research ideas.
  • Minimum education: bachelor's degree or similar experience.

Responsibilities

  • Design experiments to understand how reinforcement-learning performance scales with compute, model size, data, and reward quality, building on work such as ScaleRL.
  • Develop reinforcement-learning algorithms across policy optimization, advantage estimation, exploration, and credit assignment for long-horizon reinforcement-learning tasks.
  • Build adaptive sampling and curriculum methods that adjust task difficulty, problem selection, and rollout counts as models improve.
  • Study bias and stability during reinforcement-learning training, including importance-sampling corrections and methods to address policy staleness and training–inference mismatch.
  • Improve compute efficiency across training and inference by experimenting with hyperparameters such as length penalties, rollout counts, batch sizes, and update schedules.

Desired Qualifications

  • Early-career candidates, including final-year PhD students, are encouraged to apply.
  • A one-year postdoctoral appointment may be offered for this profile.

About the Company

Periodic Labs

AI-driven materials discovery and design

Periodic Labs uses artificial intelligence to model, predict, analyze, and design new materials. Its platform learns from large scientific datasets to suggest viable material compositions, structures, and processing methods that meet performance targets. The team combines AI expertise from OpenAI and DeepMind, applying it specifically to materials science to offer data-driven material design for energy, electronics, and manufacturing. The company aims to accelerate materials discovery, enabling clean energy, better semiconductors, and stronger supply chains by reducing the time and cost of traditional research.

Company size
51-200
Company stage
Seed
Total funding
$300M
Headquarters
Menlo Park, CA
Founded
2025

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