# Research Scientist, Scaling RL

**Company:** [Periodic Labs](https://zerogtalent.com/ai-companies/periodic-labs)
**Location:** Menlo Park, CA; Montreal, Canada
**Work mode:** On-site
**Seniority:** Mid
**Employment type:** Full Time
**Salary:** USD 225K–350K/yr
**Category:** Science
**Department:** Engineering
**Listed:** 2026-10-01

[View and apply on Zero G Talent](https://zerogtalent.com/ai-jobs/periodic-labs/research-scientist-scaling-rl-20b122c9-b8ec-4fb0-aaf4-9b45902affe0)

## What you'll do

- Design experiments to understand RL scaling with compute, data, reward quality
- Develop RL algorithms: policy optimization, advantage estimation, exploration, credit assignment
- Build adaptive sampling and curriculum methods for task difficulty and rollouts
- Study bias and stability in RL training, including importance-sampling corrections
- Improve compute efficiency via hyperparameter experiments (length penalties, rollout counts, batch sizes)

## What you need

- 5+ yrs experience in RL/LLM training
- Hands-on training LLMs with reinforcement learning
- Design small-scale RL setups transferring to large scale
- Implement, debug, test ideas in complex training stack
- Rigorous scientific approach to problem-solving

## Job description

### **About Periodic Labs**

We're an AI and physical sciences company building state-of-the-art models to accelerate breakthroughs across materials, energy, and beyond. Backed by world-class investors and growing rapidly, we operate at the pace the frontier requires. Our team brings deep expertise, genuine ownership, and a drive to push the boundaries of what's scientifically possible.

  

### About the Role

We're training frontier models to develop deep scientific knowledge and reasoning for scientific tasks. You’ll study how RL scales with training compute, develop better algorithms, and take ideas from controlled experiments to our largest runs like [Periodic Neon](https://periodic.com/news/nature-is-our-learning-environment).

  

### What You'll Do

-   Design experiments to understand how RL performance scales with compute, model size, data, and reward quality, building on work such as [ScaleRL](https://arxiv.org/abs/2510.13786)
    
-   Develop better RL algorithms, spanning policy optimization, advantage estimation, exploration, and credit assignment for long-horizon RL tasks
    
-   Build adaptive sampling and curriculum methods that adjust task difficulty, problem selection, and the number of rollouts as models improve
    
-   Study bias and stability during RL training, including importance-sampling corrections and methods to tackle policy staleness and training–inference mismatch, as [discussed here](https://www.youtube.com/watch?v=GH4JCdAAUYg).
    
-   Improve compute efficiency across training and inference through experiments with hyperparameters, such as length penalties, rollout counts, batch sizes, and update schedules.
    

  

### You Will Thrive in This Role If You Have

-   Hands-on experience training LLMs with reinforcement learning
    
-   Strong attention to detail and rigorous approach to answer questions scientifically.
    
-   Coming up with small-scale RL setups that transfers to large-scale training runs.
    
-   Comfort working across a complex training stack to implement, debug, and test new research ideas.
    
-   **We are open to offering a 1 year post-doc for this profile. Early career (final year PhD students) are encouraged to apply.**
    
      
    

**Mechanics**  
**Minimum education:** Bachelor’s degree or similar experience

**Location:** Menlo Park, CA or Montreal, Canada

**Compensation:** $225,000-$350,000 base + equity

**Visa sponsorship:** Yes, we sponsor visas and will do everything we can to assist in this process.

## Other open roles at Periodic Labs

- [Process Safety Engineer](https://zerogtalent.com/ai-jobs/periodic-labs/process-safety-engineer-1d57d49b-e538-4d9a-86d3-6be17339016f) — Menlo Park, CA
- [Controller](https://zerogtalent.com/ai-jobs/periodic-labs/controller-6c188bbb-1f81-4145-aac3-28cb0e47e3cc) — Menlo Park, CA
- [Research Scientist/Research Engineer, Midtraining](https://zerogtalent.com/ai-jobs/periodic-labs/research-scientist-research-engineer-midtraining-168dbf20-2230-49a0-9f00-f2ff58dad5c8) — Menlo Park, CA
- [XRD Facility Manager](https://zerogtalent.com/ai-jobs/periodic-labs/xrd-facility-manager-e82a30cb-ad71-402f-b1c2-1b762fc50bfe) — Menlo Park, CA
- [Computational Scientist, Differentiable Physics](https://zerogtalent.com/ai-jobs/periodic-labs/computational-scientist-differential-physics-ccdb34b1-b67c-4dbf-8acb-fe786f2ab38b) — Menlo Park, CA

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*Source: [Zero G Talent](https://zerogtalent.com), the job board for space, defense, AI, robotics and frontier-tech companies.*