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Product Engineer

Periodic Labs· Engineering
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Menlo Park, CA$250k - $350kFull-timePosted 11mo ago

About the role

We’re hiring a product engineer to build the agentic software layer between frontier models, scientific data, lab systems, and scientists, turning cutting-edge models into products that integrate with real scientific workflows.

Responsibilities

  • Prototype quickly and ship continuously — build high-leverage products that become part of scientists’ daily workflow, then refine them through direct feedback from the lab and from customers
  • Build agentic products and interfaces on top of our frontier models and scientific data, including copilots, experiment planning systems, workflow automation, operational tools, and new interaction patterns that go beyond chat
  • Design how scientists direct, supervise, and collaborate with AI agents across experiment planning, execution, analysis, and decision-making
  • Work closely with model researchers to shape product behavior, evaluation signals, telemetry, and feedback loops that improve both the product experience and the underlying models
  • Own the product end to end, from data model and backend systems to APIs, user experience, deployment, and observability
  • Spend time with scientists in the lab, understand where time and attention are being lost, and turn those bottlenecks into products before they are formally requested
  • Help define the product engineering culture at Periodic, including standards for speed, quality, ambition, and taste

Qualifications

  • Minimum experience: 5+ years
  • Minimum education: Bachelor’s degree or similar experience

Skills

Required:Experience with product engineering in an AI/ML contextSystems design for end-to-end product ownershipClose collaboration with researchers and scientists
Last updated Oct 7, 2026. Interested? Apply on the company site.

About Periodic Labs

From bits to atoms.

Periodic Labs is an artificial intelligence research company pioneering the next leap in scientific discovery. We operate at the intersection of machine learning and scientific experimentation under the thesis that that true understanding of the universe is only possible by interacting and experimenting on it.

artificial intelligencescientific discoverymachine learningscientific experimentation
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