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

As Periodic Labs scales the experimental engine behind our AI-driven materials discovery platform, we’re hiring a Process Engineer to own the end-to-end workflow of our powder synthesis lab. You’ll work directly across dispensing, mixing, pressing, reaction, recovery, and characterization — building the systems, controls, and instincts that keep a high-mix, fast-moving research process producing trustworthy data at increasing throughput.

This is a hands-on, jack-of-all-trades role. You’ll move fluently between equipment troubleshooting, statistical process control, operator workflows, and the underlying science. The variation here is intentional — we run an enormous diversity of chemistries — and your job is to build the systems that absorb that intended variation while ruthlessly separating out the unintended kind. What you learn stabilizing the manual lab will directly shape how we design our automated, full-scale lab.

What You’ll Do

Own the powder synthesis workflow end-to-end — dispensing, mixing, pressing, reaction, recovery, and characterization — ensuring the process runs smoothly as throughput and complexity grow

Map failure modes across the full process flow and design comprehensive systems and controls so failures either don’t happen or are caught and recovered quickly

Apply statistical process control (SPC) and systems thinking to track key process inputs and outputs, identify drift, and bring processes into a stable, predictable state

Troubleshoot and diagnose equipment issues across the lab — dispensing heads, reaction chambers, characterization tools — and drive root-cause investigations to durable fixes

Investigate data integrity issues across the toolchain, including cases where equipment dispenses correctly but uploads data incorrectly, and partner with software and infra to adjust scripts and instrumentation

Design operator-facing workflows that prevent sample mix-ups and reduce variation introduced by human handoffs

Run design of experiments (DOE) to identify which recipe and process factors most impact outcomes, and use structured methodologies to converge efficiently

Partner with research scientists, lab engineers, and software teams to translate the lessons from the manual lab into requirements for the automated, full-scale lab

Establish quality and reliability practices that scale with us — process documentation, control plans, and feedback loops between operators, scientists, and the data systems

You Will Thrive in This Role If You Have

A degree in chemistry, materials science, chemical engineering, or a related field, with hands-on experience in solid-state chemistry, ceramics, or powder processing

Experience as a process engineer (or closely adjacent role) in a high-mix R&D, pilot, or manufacturing environment — ideally one that involved both intentional recipe variation and tight process discipline

Strong systems thinking — you can break an unfamiliar process into clear inputs, outputs, and unit operations, and quickly build mental models of how the black boxes inside actually behave

Working knowledge of statistical process control (SPC), six sigma or lean concepts, and design of experiments (DOE), with comfort using tools like JMP or Minitab

Hands-on familiarity with characterization techniques relevant to powders and ceramics — e.g., X-ray diffraction (XRD), scanning electron microscopy (SEM) — and with high-temperature processing of ceramic materials

Comfort with equipment troubleshooting and a healthy bias toward getting your hands on the tool, the data, and the operator workflow when something goes wrong

Some computer savvy — enough to read and lightly modify scripts, validate data flows, and partner effectively with software engineers when instrumentation or data pipelines need adjustment