---
title: 'Software Engineer at Periodic Labs'
canonical: 'https://feeny.ai/job/software-engineer-periodic-labs-menlo-park-e2jbdwfj11fn'
type: 'job'
last_seen: '2026-11-09'
---

# Software Engineer at Periodic Labs

- **Company:** Periodic Labs
- **Location:** Menlo Park, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-02-10
- **Last confirmed live:** 2026-11-09
- **Apply:** https://jobs.ashbyhq.com/periodic-labs/1d6ade4f-ae4c-4bde-baf7-82c401063e42

## 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

We’re hiring software engineers to build the backend and distributed systems that form the software backbone of an AI-native physical science lab. These systems turn scientific intent into reliable execution: scheduling large simulation workloads, orchestrating experiments and shared equipment, and connecting instruments and automation to the rest of our software. Depending on your background and interests, you may work across simulation infrastructure, lab orchestration, or automation systems. You do not need prior experience in every area. We care most about strong engineers who can reason about complex systems, make failure visible, and build software that remains reliable as scientific work scales. You’ll work directly with scientists, infrastructure engineers, and lab engineers to understand how research gets done, then turn prototypes and manual workflows into robust systems without slowing down discovery.

## What You’ll Do

- Design and build backend and distributed systems that execute scientific work reliably.
- Build scheduling and orchestration for long-running workflows across compute clusters, instruments, and other shared resources.
- Create clear APIs, data models, and service boundaries connecting research software, lab information systems, instruments, and automation.
- Make execution inspectable and recoverable through durable state, retries, failure handling, observability, and provenance.
- Turn promising research prototypes and scientific workflows into maintainable production systems.
- Diagnose bottlenecks and failures across application code, infrastructure, data, and hardware integrations.
- Work closely with scientists and engineers to improve experimental throughput, reliability, and reproducibility.

You Will Thrive in This Role If You Have Experience With

- Strong software engineering fundamentals and a track record of building production backend or distributed systems.
- Experience with asynchronous or long-running work, such as batch jobs, workflow orchestration, data pipelines, schedulers, or shared-resource systems.
- The ability to reason clearly about concurrency, idempotency, partial failures, consistency, and resource contention.
- Experience designing maintainable APIs and data models for complex systems.
- Strong debugging skills across services, infrastructure, dependencies, and data.
- Comfort working with technical collaborators to turn ambiguous needs into reliable systems.

Especially Strong Candidates May Also Have You do not need experience in every area below. Depth in one or more may help us identify where you can have the greatest impact.

- Large-scale batch or distributed compute, scientific simulation, GPU workloads, or ML infrastructure.
- Workflow engines, DAG orchestration, job schedulers, or cluster execution systems.
- LIMS, MES, ERP, process-control systems, lab informatics, or scientific data management.
- Robotics, hardware controls, instrument drivers, vendor APIs or SDKs, or industrial automation.
- Experience in a physical lab, manufacturing environment, or materials-science setting.

Mechanics Minimum education: Bachelor’s degree or similar experience Location: Menlo Park, CA 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)

## Other roles at Periodic Labs

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- [Process Safety Engineer](https://feeny.ai/job/process-safety-engineer-periodic-labs-menlo-park-kzcpxh3d9mg6) — Menlo Park, CA
- [Controller](https://feeny.ai/job/controller-periodic-labs-menlo-park-y8xmsezeq3ch) — Menlo Park, CA
- [Automation Engineer](https://feeny.ai/job/automation-engineer-periodic-labs-menlo-park-e7a6p1rtctm5) — Menlo Park, CA
- [Research Scientist, Scaling RL](https://feeny.ai/job/research-scientist-scaling-rl-periodic-labs-menlo-park-g9s9gctkbnjs) — Menlo Park, CA / Montréal, Canada
- [Research Scientist/Research Engineer, Midtraining](https://feeny.ai/job/research-scientist-research-engineer-midtraining-periodic-labs-menlo-park-gh5qfkb8vxth) — Menlo Park, CA
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