---
title: 'Research Scientist, Data at Periodic Labs'
canonical: 'https://feeny.ai/job/research-scientist-data-periodic-labs-menlo-park-qbeeqgcbrtaw'
type: 'job'
last_seen: '2026-09-07'
---

# Research Scientist, Data at Periodic Labs

- **Company:** Periodic Labs
- **Location:** Menlo Park, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-07-25
- **Last confirmed live:** 2026-09-07
- **Apply:** https://jobs.ashbyhq.com/periodic-labs/d039ec58-a97d-47e1-b1e5-db003d772f77

## Job description

## ABOUT PERIODIC LABS

The most important scientific discoveries of our time won’t happen in a traditional lab. 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 an insatiable drive to push the boundaries of what’s scientifically possible.

## ABOUT THE ROLE

You will work on the most important aspect of Scientific AI creation: evaluations and data. This means constructing cutting-edge evaluations based on advanced scientific use cases, sourcing and procuring external datasets, integrating internally generated experimental data into the training stack, constructing training environments for RL. You’ll ensure that the team always has the right assets, in the right shape, to evaluate and improve AI models.

You will work with computational and experimental scientists to translate complex scientific workflows into rigorous evaluations and agentic benchmarks, and partner with pretraining, midtraining, and reinforcement learning researchers to identify the data models needed, then build the datasets, environments, and pipelines to deliver it. Your goal will be to create a tight feedback loop between scientific use cases, model evaluation, and training data.

## WHAT YOU’LL DO

- Own the evaluation and data strategy across the training stack, identifying capability gaps and shaping the roadmap with leads of physical science and AI research
- Work with domain experts to translate advanced scientific workflows into rigorous evals, benchmarks, and RL environments
- Source, evaluate, and procure external datasets across chemistry, physics, materials science, mathematics, simulations, and laboratory instrumentation
- Build robust pipelines to ingest, clean, and transform for training large-scale datasets from heterogeneous sources
- Build tooling and analysis workflows that help researchers inspect data, understand model failures, and determine which evaluations or datasets to develop next

YOU WILL THRIVE IN THIS ROLE IF YOU HAVE

- Designed evaluations, benchmarks, or RL environments for language models, agents, or scientific AI systems
- Built large-scale data pipelines for LLM pretraining, midtraining, post-training, or evaluation
- Strong judgment about dataset and evaluation quality, including scientific relevance, coverage, provenance, licensing, and contamination risks
- Strong software and data engineering skills, including familiarity with data processing at scale, dataset versioning, lineage tracking
- A research-oriented mindset: you form hypotheses about data, run controlled experiments, measure model outcomes, and iterate with rigor
- Research experience in areas such as materials science, solid state chemistry, chemistry, computational physics, semiconductors

## MECHANICS

Minimum education: Bachelor’s degree or similar experience

Location: Menlo Park, CA or Montreal, Canada. (Soon: San Francisco, too)

Compensation: $250,000-350,000 + equity

Visa sponsorship: Yes, we sponsor visas.

## About Periodic Labs

## Company Overview
- **One-liner**: Periodic Labs is building AI scientists and autonomous laboratories to accelerate scientific discovery in the physical sciences, starting with materials design and semiconductor research.
- **Entity Type**: Private (seed-stage, $300M total funding raised in 2025)
- **Headquarters**: San Francisco, California, USA (primary) and Menlo Park, California, USA (dual locations) [cbinsights.com](https://www.cbinsights.com/company/periodic-labs), [linkedin.com](https://www.linkedin.com/company/periodic-labs)
- **Founded**: 2025 [cbinsights.com](https://www.cbinsights.com/company/periodic-labs)
- **Founders**: Ekin Dogus Cubuk (Co-Founder) and other undisclosed founding team members with backgrounds at OpenAI, DeepMind, and Google [linkedin.com](https://www.linkedin.com/company/periodic-labs), [periodic.com](https://periodic.com/)

## Core Business
- **Primary industry/industries**: Physical sciences R&D, artificial intelligence, autonomous laboratory systems
- **Target customers**: B2B – enterprises in semiconductor manufacturing, materials science, energy, and other deep-tech sectors requiring experimental research
- **Mission or purpose statement**: “Our goal is to create an AI scientist” that can autonomously hypothesize, run experiments, and learn from results to discover new materials and accelerate technological progress [periodic.com](https://periodic.com/)

## Products & Services
- **AI Scientist**: A suite of AI models trained to generate hypotheses, design experiments, and interpret results across physical science domains.
- **Autonomous Laboratories**: Fully automated, high-throughput experimental platforms that produce large volumes of high-quality data (including negative results) to train AI models. [periodic.com](https://periodic.com/)
- **Custom Agents for Industry**: Tailored AI agents for engineers and researchers, e.g., helping a semiconductor manufacturer analyze heat dissipation data to accelerate iteration. [periodic.com](https://periodic.com/)

## Market Standing
- **Valuation/Market Cap**: Not disclosed (private seed-stage company)
- **Key Metric**: Total funding raised – $300M in a single seed round (2025) [cbinsights.com](https://www.cbinsights.com/company/periodic-labs)
- **Notable Investors/Partners**: a16z, Felicis, DST, NVentures (NVIDIA’s venture arm), Accel, Emerson Collective, Fellows Fund, Coatue; individual backers include Jeff Bezos, Elad Gil, Eric Schmidt, Jeff Dean. Also collaborating with academic advisors from Stanford, Northwestern, and more. [periodic.com](https://periodic.com/), [cbinsights.com](https://www.cbinsights.com/company/periodic-labs)
- **Growth Signals**: 40 employees as of mid-2026, with monthly headcount growth of +19.6% and 25 active job postings (+47.1% quarterly growth) [linkedin.com](https://www.linkedin.com/company/periodic-labs)

## Competitive Advantages
- **Proprietary data generation**: Autonomous labs produce massive, unique datasets (including valuable negative results) that do not exist in public literature, creating a data moat.
- **Founding team pedigree**: Core team co-created ChatGPT, DeepMind’s GNoME, OpenAI’s Operator (Agent), MatterGen, and scaled autonomous physics labs – rare concentration of AI + science talent. [periodic.com](https://periodic.com/)
- **End-to-end integration**: AI models, simulation, and physical experimentation are all in-house, enabling rapid closed-loop learning.

## Strategic Focus
- **Scale autonomous labs**: Expand capacity to generate experimental data across multiple physical science domains.
- **Target high-impact problems**: Superconductor discovery, semiconductor heat dissipation, and other materials challenges that can unlock Moore’s Law, advanced energy grids, and space travel. [periodic.com](https://periodic.com/)
- **Develop the first generation of AI scientists**: Train models that can operate independently of human input across the entire scientific method.

## Why Work Here
- **Culture**: “From bits to atoms” – a deep-tech startup blending frontier AI with hands-on lab work. Emphasis on rapid iteration and real-world impact.
- **Work policy**: In-office preferred; primary offices in Menlo Park and San Francisco, CA. Most roles are on-site. [builtin.com](https://builtin.com/company/periodic-labs)
- **Notable perks**: Work alongside world-class AI researchers and experimental scientists; access to cutting-edge automated lab equipment; early-stage equity and high-growth trajectory.
- **Engineering culture**: 34% of employees are in technical roles; talent sourced from OpenAI, Google DeepMind, Meta, xAI, and Stanford – strong peer learning environment. [linkedin.com](https://www.linkedin.com/company/periodic-labs)

## Sources
1. [periodic.com](https://periodic.com/)
2. [builtin.com](https://builtin.com/company/periodic-labs)
3. [cbinsights.com](https://www.cbinsights.com/company/periodic-labs)
4. [linkedin.com](https://www.linkedin.com/company/periodic-labs)
5. [jobs.ashbyhq.com/periodic-labs](https://jobs.ashbyhq.com/periodic-labs)

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