Process Engineer, Powder

Periodic Labs•Menlo Park, CA

5d•Hybrid

About The Position

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.

Requirements

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
Strong ownership, bias for action, and the judgment to know when to stabilize what exists versus when to redesign it
Clear communication and the ability to work cross-functionally with researchers, lab operators, equipment vendors, and software teams

Nice To Haves

Experience at companies working on battery materials, anode/cathode chemistries, or other powder- and ceramic-heavy processes (e.g., Tesla, Sila, Mitra Chem, 24M, or similar)
Background spanning multiple unit operations — you’ve had to come up to speed quickly on processes you didn’t design
Experience scaling an R&D or pilot process toward higher throughput or automation
Exposure to quality engineering, reliability engineering, or process development roles, and an interest in growing into deeper specialization over time
Familiarity with how scientific instrumentation integrates with data systems, and an instinct for separating equipment problems from data-pipeline problems

Responsibilities

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