---
title: 'Research Associate, Thin Films at Periodic Labs'
canonical: 'https://feeny.ai/job/research-associate-thin-films-periodic-labs-menlo-park-0j19sqqq96es'
type: 'job'
last_seen: '2026-09-07'
---

# Research Associate, Thin Films at Periodic Labs

- **Company:** Periodic Labs
- **Location:** Menlo Park, CA
- **Employment:** contract
- **Work type:** onsite
- **Posted:** 2026-03-10
- **Last confirmed live:** 2026-09-07
- **Apply:** https://jobs.ashbyhq.com/periodic-labs/98837207-657f-4116-a112-d8dc6184b49e

## 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 an insatiable drive to push the boundaries of what’s scientifically possible.

## ABOUT THE ROLE

Join our world-class team of scientists and engineers building a lab where AI and automation speed up physical R&D.

Periodic Labs is developing AI that can both simulate science and verify its predictions to train on the full scientific method. A key challenge is the gap between bulk materials discovery and thin-film form: materials our AI predicts and our powder lab synthesizes must ultimately be validated as scalable thin films to be relevant to semiconductor, memory, and advanced materials applications. We are building a dedicated thin-film lab while generating early data at partner facilities.

We're looking for a hands-on Research Associate in Thin Films to run advanced deposition and nanofabrication processes, generate high-quality experimental data, and collaborate closely with our AI and materials teams. This is a 12-month fixed-term position with the potential to convert to full time.

In the near term you will operate at external partner facilities before Periodic’s own tools come online. As the in-house lab is commissioned, you will transition to running experiments on deposition and metrology suite.

## WHAT YOU’LL DO

- As a process module owner, you will execute thin-film deposition and related nanofabrication processes.
- Prepare substrates, manage process flows, and maintain detailed experimental records that meet the metadata and data quality standards for AI training. Every experiment you run is a potential data point for our models - documentation quality matters as much as deposition quality.
- Perform structural and functional thin-film characterization: XRD/XRR for structure and thickness, ellipsometry and profilometry for film properties, SEM/EDX for morphology and composition, and 4-point probe and basic transport measurements for electrical properties.
- Monitor in-situ metrology during deposition to ensure high quality film growth.
- Collaborate with the AI and materials science teams to help define which deposition parameters to vary, interpreting film characterization results in context of what the AI predicts, and flagging discrepancies that may indicate new physics or synthesis insights.
- Troubleshoot process issues and iterate quickly on recipes under guidance from senior team members. Escalate anomalies rather than work around them, and document both failures and fixes in a format that preserves institutional knowledge.

YOU WILL THRIVE IN THIS ROLE IF YOU HAVE

- Currently pursuing or recently completed a PhD (or advanced graduate degree) in materials science, physics, chemistry, or a related field.
- Hands-on experience operating thin-film deposition equipment - sputtering, evaporation, PLD, ALD, CVD, MBE, or related techniques - developed in a university or research lab environment.
- Familiarity with the practical realities of these systems - target conditioning, chamber qualification, substrate preparation, and recipe troubleshooting.
- Basic thin-film characterization experience: you know how to read an XRD pattern, interpret an ellipsometry fit, and recognize a SEM image that signals a process problem.
- Strong documentation habits and attention to detail. You log what you did, not just what you intended to do, and you understand why that distinction matters in a data-driven science environment.
- Ability to ramp up quickly on new equipment and experimental workflows, and comfort operating independently in shared research facilities where you are responsible for your own training and access.

## ESPECIALLY STRONG CANDIDATES MAY ALSO HAVE

- Experience working in university nanofabrication facilities or shared cleanroom environments — including completing facility-specific safety training, navigating tool reservation systems, and operating within shared-use norms.
- Exposure to functional materials in thin-film form: superconductors, magnetics, ferroelectrics, thermoelectrics, or multi-layer device stacks relevant to memory or semiconductor applications.
- Familiarity with LIMS or other lab information systems used to track samples, experiments, and characterization results.
- Experience with wafer-level metrology: film thickness mapping, stress/warpage measurement, or 4-point probe resistivity mapping at wafer scale.
- Interest in working at the intersection of experimental science and AI-driven discovery.

## MECHANICS

Minimum education: Bachelor’s degree or similar experience

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

Compensation: $180,000–$225,000

This is a 12-month fixed-term position, with the potential to convert to full time.

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

## 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)

## Other roles at Periodic Labs

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- [Technical Recruiter](https://feeny.ai/job/technical-recruiter-periodic-labs-menlo-park-04dyj1a17h1w) — Menlo Park, CA
- [Business Operations, Product & Science](https://feeny.ai/job/business-operations-product-science-periodic-labs-menlo-park-bqebn8py4dnh) — Menlo Park, CA
- [Process Technician, Thin Films](https://feeny.ai/job/process-technician-thin-films-periodic-labs-menlo-park-v0m9kw9t9506) — Menlo Park, CA
- [Research Engineer, Lab Automation](https://feeny.ai/job/research-engineer-lab-automation-periodic-labs-menlo-park-x22bb9y5rh0q) — Menlo Park, CA
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