Research Scientist/Engineer, Materials

Periodic Labs; Menlo Park (CA); Hybrid; USD 250,000 - 350,000; Full time

Benefits offered by this job

Visa sponsorship: Yes

Job summary

Periodic Labs in Menlo Park is seeking a materials scientist to advance AI-driven research across materials discovery. You will work with experimental and computational teams, building agents and tools, and focus on data that enables LLMs to reason and improve through addressing data quality and metadata relevance.

This hybrid role requires deep domain knowledge, meticulous attention to detail, and the ability to fix data at the source while aligning research objectives with practical lab

Qualifications

• Minimum 4+ years in materials science research or industry
• PhD in Materials Science, Chemistry, or related field, or equivalent industry experience
• Experience with data at scale and lab-data workflows

Responsibilities

• Collaborate with lab scientists and computational teams on active research problems
• Identify data errors and failure modes in agents and fix at source
• Restructure materials databases around how agents reason and fail
• Build research software for lab environments and evaluate agent performance

Skills

Materials science

Data at scale

Experimental methods

Computational modeling

LLM tooling

Education

PhD in Materials Science, Chemistry, or related field

Tools

Python

SQL / databases

Lab data management

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

At Periodic Labs, we are automating scientific research in materials discovery; the data we collect, and how we represent it, is what makes this possible. We are hiring a materials scientist to work across the entire process: to find where our agents and data fail, and to fix those failures at the source. This is a hybrid research and infrastructure role. Roughly half your time will be spent working directly with our experimental and computational scientists, building agents and tools that solve active research problems. The other half will be spent improving the data those agents depend on.

This role requires domain depth, not just data engineering skill. You need to know, for example, what metadata actually matters for a given characterization technique. The research half of the role keeps you grounded in the problems we are actually using LLMs to solve, so the schemas you build serve real research rather than an abstraction of it. The work demands attention to detail and a willingness to get into the weeds: to meticulously read, understand, and improve our data. You'll be as much a materials scientist doing research as the person who makes our agentic harness actually work.

What You'll Do

• Work directly with lab scientists and the computational team on active research problems, staying close to the actual bottlenecks LLM agents are meant to solve.

• Investigate agent traces to identify data errors and agentic failure modes, and eliminate them at the source.

• Restructure and re-architect our materials databases (lab experiments, characterization data, computations) around how LLM agents actually reason and fail, not just around human readability. Draw on domain expertise about how to represent the data and what metadata matters.

• Work with the hardware and automation teams to make data collection more robust.

• Build research software for lab environments, translating scientific requirements into working tools.

• Distill what you learn into evaluations that measure agent performance.

Mechanics

• Minimum experience: 4+ years of research or industry experience in experimental or computational materials science and working with data at scale.

• Minimum education: PhD in Materials Science, Chemistry, or a related field, or equivalent industry experience.

• Location: Menlo Park, CA (Soon: San Francisco, too)

• Compensation: $250,000-350,000 + equity

• Visa sponsorship: Yes, we sponsor visas.