OpenAIBuilds for teams inside the company
Software Engineer, GTM Growth Engineering
San Francisco, CAOn-site
$266,000–$405,000
Updated
$250,000–$350,000Menlo Park, CAOn-site
Product Engineer building the agentic software layer connecting frontier models, scientific data, lab systems, and materials scientists.
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$250K–$350KAbove the median
Median $188K
This job$250K–$350KTypical senior FDE payMiddle 50% of 1,748 postings$160K–$220KBuilds for teams inside the company
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Posted by Periodic Labs on
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.
We’re hiring a product engineer to build the agentic software layer between our frontier models, our scientific data, our lab systems, and the scientists using them. You’ll build the environment where materials scientists work alongside AI agents to plan experiments, run analyses, coordinate workflows, and turn results into decisions. The right person for this role is excited to define what the working interface between scientists and AI should actually look like.
You’ll turn cutting-edge models into products that become part of real scientific workflows, both inside Periodic and in customer environments. That means designing interfaces, orchestrating tools and agents, integrating with lab and data systems, and building the feedback loops that improve both product behavior and model performance.
As one of our first product hires, you’ll help define product engineering at Periodic: technically deep, ambitious in scope, and tightly coupled to real scientific work.
Minimum experience: 5+ years
Minimum education: Bachelor’s degree or similar experience
Location: Menlo Park, CA
Compensation: $250,000-$350,000 base + equity
Visa sponsorship: Yes, we sponsor visas and will do everything we can to assist in this process.
AI scientists running autonomous robotic labs that synthesize and characterize materials, training models on real-world experimental feedback to accelerate discovery.
Seed · 50–100 people
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