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Periodic Labs

AI-driven materials discovery and design

Business Operations - Product & Science

Business OperationsConfirmed live in the last 24 hours
Full-timeIn-person

About the Job

Requirements

  • An undergraduate or graduate degree in physics, chemistry, materials science, computational science, or a closely related quantitative discipline, or equivalent depth demonstrated through research or industry experience.
  • At least 4 years of experience in strategy, operations, product management, chief-of-staff, or a closely related function, with some experience inside a technical, research-driven, or AI organization.
  • Ability to read and engage with primary scientific literature, follow technical discussions at the frontier of AI and physical science, and translate findings into operational decisions.
  • Ability to decompose complex problems, build models under uncertainty, and present findings clearly and rigorously.
  • Ability to communicate precisely in writing and adapt communication for audiences ranging from principal scientists to board members.
  • High agency, sound judgment, and credibility to influence across researchers and engineers without authority.
  • Experience designing and operating planning and prioritization systems in exploratory, uncertain, high-stakes environments.
  • A genuine interest in using AI to accelerate scientific discovery.
  • Bachelor’s degree or similar experience.

Responsibilities

  • Support the research organization’s operating rhythm, including research planning cycles, milestone tracking, experiment prioritization, and coordination between AI and physical sciences.
  • Design and maintain systems that give leadership real-time visibility into research progress, resource utilization, and emerging risks across model development and lab science roadmaps.
  • Partner with the Head of Research and Principal Scientists to translate long-term scientific goals into quarterly operating plans, resourcing decisions, and hiring priorities.
  • Identify and remove organizational, process, and tooling bottlenecks that slow researchers and scientists.
  • Coordinate compute, lab equipment, and infrastructure resource allocation across research projects with infrastructure and lab operations leads.
  • Connect research outputs with product development, translating scientific milestones into product implications and making product needs clear to research teams.
  • Drive product planning with founders and product leads, including roadmap development, prioritization frameworks, and cross-functional alignment.
  • Build roadmaps, decision logs, specification templates, and review cadences to scale product-science collaboration.
  • Lead or support evaluation design, benchmark development, and model assessment connecting scientific capability to real-world product requirements.
  • Lead special projects and strategic analyses, including competitive landscape assessments, build/buy/partner decisions, and new capability evaluations.
  • Support external partnerships, collaborations, and customer engagements at the boundary of research and commercial work, from scoping through execution.

Desired Qualifications

  • A PhD in a physical science or engineering discipline, with research experience at the boundary of computation and experiment, such as molecular simulation, condensed matter, or materials informatics.
  • Prior experience in research operations, technical program management, or science strategy at an AI lab, national lab, or research-intensive technology company.
  • Hands-on experience with AI/ML workflows, model evaluation, or the product development lifecycle for AI-powered applications.
  • Experience spanning the scientific and commercial sides of a technology organization, working with both researchers and customers.
  • Experience building or scaling operational functions from scratch, including hiring, process design, and tooling selection.

About the Company

Periodic Labs

AI-driven materials discovery and design

Periodic Labs uses artificial intelligence to model, predict, analyze, and design new materials. Its platform learns from large scientific datasets to suggest viable material compositions, structures, and processing methods that meet performance targets. The team combines AI expertise from OpenAI and DeepMind, applying it specifically to materials science to offer data-driven material design for energy, electronics, and manufacturing. The company aims to accelerate materials discovery, enabling clean energy, better semiconductors, and stronger supply chains by reducing the time and cost of traditional research.

Company size
51-200
Company stage
Seed
Total funding
$300M
Headquarters
Menlo Park, CA
Founded
2025

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