Business Operations, Product & Science

Periodic Labs•Menlo Park, CA

7d•$250,000 - $300,000•Hybrid

About The Position

Periodic Labs is an AI and physical sciences company focused on accelerating scientific breakthroughs. As Business Operations, you will be the connective tissue between research teams, the AI model development roadmap, and the company's strategic and operational needs. This role involves working cross-functionally to translate scientific progress into tangible outcomes. It requires operating in technical environments, understanding how science and AI models are developed, and building organizational systems to expedite this process. You will assist with product-science coordination and act as a strategic partner to leadership. The ideal candidate has a graduate-level scientific background (physics, chemistry, materials science, or related field) and experience in strategy, operations, or product roles within a technology or research organization. Strong analytical skills, exceptional communication, and the ability to build trust with both scientists and executives are essential.

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.
7+ years of experience in strategy, operations, product management, chief-of-staff, or a closely related function, with at least some experience within a technical, research-driven, or AI organization.
Ability to read and engage with primary scientific literature, follow technical discussions in AI and physical science, and translate learnings into operational decisions.
Exceptional analytical and structured thinking skills.
Outstanding communication skills, including precise and clear writing and the ability to adapt communication style for different audiences.
High agency, strong judgment, and organizational credibility to influence without authority.
Experience designing and operating planning and prioritization systems in exploratory environments with uncertain outputs and high stakes.
A genuine belief in the mission of using AI to accelerate scientific discovery.

Nice To Haves

A PhD in a physical science or engineering discipline with research experience at the boundary of computation and experiment (e.g., molecular simulation, condensed matter, materials informatics).
Prior experience in a research operations, technical program management, or science strategy role 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.
A background spanning both the scientific and commercial sides of a technology organization.
Experience building or scaling operational functions from scratch, including hiring, process design, and tooling selection.

Responsibilities

Support the operating rhythm of the research organization, including research planning cycles, milestone tracking, experiment prioritization, and cross-team coordination between AI and physical sciences.
Design and maintain systems for real-time leadership 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-horizon scientific goals into quarterly operating plans, resourcing decisions, and hiring priorities.
Identify and remove organizational, process, and tooling bottlenecks that slow down researchers and scientists.
Coordinate compute, lab equipment, and infrastructure resource allocation across research projects.
Serve as the operational link between research outputs and product development, ensuring scientific milestones translate into clear product implications and product needs are understood by research teams.
Drive the product planning process, including roadmap development, prioritization frameworks, and cross-functional alignment.
Build scalable processes and artifacts for product-science collaboration, such as roadmaps, decision logs, spec templates, and review cadences.
Lead or support evaluation design, benchmark development, and model assessment processes 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 activities.