Customer Engineer

Periodic Labs•Menlo Park, CA

13h•$300,000 - $400,000•Hybrid

About The Position

Periodic Labs is an AI and physical sciences company building state-of-the-art models to accelerate breakthroughs across materials, energy, and beyond. Unlike typical Customer Engineer roles focused on software deployment, this position at Periodic Labs involves deploying complex AI models trained on physical science, connected to a live autonomous lab, to solve challenging materials and process engineering problems. This is a pioneering role, being the first of its kind at the company, and requires the individual to define their own playbook. The customers are large industrial organizations in sectors like semiconductor advanced packaging, aerospace, energy, and advanced manufacturing, possessing extensive process data and high-value problems. The Customer Engineer will embed with these customers, build technical trust, rapidly scope problems that align with Periodic's AI and lab capabilities, and then work to solve them. This is a proactive, front-line role, not a support function. The role requires regular on-site presence at customer facilities such as fabs, factory floors, and engineering war rooms, working with world-class engineers on high-stakes, real-world problems. Learnings from each deployment are expected to feed back into improving Periodic's models, data pipelines, and lab workflows.

Requirements

A strong engineering or physical science background (BS/MS/PhD in materials science, chemical engineering, mechanical engineering, physics, or a closely related field) to engage as a peer with engineers in semiconductor fabs or aerospace R&D labs.
Hands-on software engineering ability, including building data pipelines, writing clean Python, working with APIs and databases, and integrating systems under time pressure.
Experience working with messy, real-world industrial or scientific data (process logs, equipment telemetry, metrology outputs, experimental records), with the ability to find signal in noise and build infrastructure for data cleanliness.
Proven ability to work directly with technical customers or stakeholders, understanding constraints, earning trust, and translating complex technical capabilities into usable solutions.
Comfort operating in high-ambiguity, high-ownership situations, requiring the ability to figure things out, build what doesn’t exist, and remain calm when faced with unfamiliar customer data.
Willingness to travel regularly and work on-site at customer facilities, including unusual environments like cleanrooms, factory floors, and high-security industrial sites.

Nice To Haves

Experience in a forward-deployed, field engineering, or embedded customer role at an industrial AI company with complex products, demanding customers, and real production deployments.
Domain depth in semiconductor process engineering, advanced packaging, aerospace structures or propulsion materials, battery or energy materials, or advanced manufacturing.
Familiarity with AI/ML workflows applied to scientific or industrial data, including training pipelines, model evaluation, inference deployment, and integrating model outputs into engineering decisions.
Experience with lab informatics systems (LIMS, MES, ELN) or industrial data infrastructure (historians, OPC UA, SCADA) and the ability to connect them to modern data and AI tooling.
Background in experiment design, DOE, or statistical process control in an applied industrial or R&D context.
Prior experience navigating IP, data confidentiality, and export control complexities within large industrial customers’ proprietary environments.

Responsibilities

Embed with customer engineering and R&D teams — in person, on site — to understand their most critical technical problems at depth, directly from the production floor or fab.
Rapidly scope and frame what Periodic can credibly solve, identifying which customer problems map onto Periodic's AI models, lab capabilities, or data pipelines, and having the judgment to identify those that do not.
Own end-to-end delivery for customer engagements, from problem definition and data ingestion, through model runs, experimental design, and results interpretation, to a deliverable the customer actually uses.
Build and adapt technical integrations on the fly, including data pipelines, API connections, experiment configuration, and model inference workflows, using Periodic’s internal stack.
Translate messy, real-world customer data (fab process records, metrology outputs, equipment logs, proprietary experimental histories) into clean inputs for Periodic’s AI models and LIMS.
Serve as Periodic’s technical face to the customer, running working sessions, presenting results to engineering leads and executives, and building deep trust for long-term partnerships.
Feed learnings back into Periodic’s product and research roadmap, acting as the primary conduit for intelligence on new requirements, data types, and problem structures surfaced during customer deployments.