The most important scientific discoveries of our time won’t happen in a traditional lab. 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

Most companies that hire Customer Engineers that are deploying software. At Periodic Labs, you’ll be deploying something more complex: AI models trained on physical science, connected to a live autonomous lab, in service of some of the hardest materials and process engineering problems on earth. This is the first hire of its kind at Periodic, and the role doesn’t come with a playbook.

Our customers are large industrial organizations — in semiconductor advanced packaging, aerospace, energy, and advanced manufacturing — sitting on decades of process data, unsolved High Value Problems, and internal teams who are skeptical of vendors by default. Your job is to embed with them, earn their technical trust, rapidly scope the right problem, and then work back into Periodic’s AI and lab capabilities to actually solve it. You are not a support function. You are the tip of the spear.

Expect to be on-site with customers regularly — in fabs, on factory floors, in engineering war rooms. Expect to work in environments where the problems are real, the stakes are high, and the people you’re working with are world-class engineers who will immediately know if you’re faking it. Bring back what you learn. Every deployment you run should make Periodic’s models, data pipelines, and lab workflows better for the next one.

What You’ll Do

• Embed with customer engineering and R&D teams — in person, on site — to understand their most critical technical problems at depth. Not from a slide deck. From standing next to them on the production floor or in the fab.

• Rapidly scope and frame what Periodic can credibly solve: which customer problems map onto our AI models, our lab capabilities, or our data pipelines — and which ones don’t. Have the judgment and the courage to say so.

• Own end-to-end delivery for your 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 — data pipelines, API connections, experiment configuration, model inference workflows — using Periodic’s internal stack. This is an engineering role.

• 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 the kind of deep trust that makes a pilot become a long-term partnership.

• Feed learnings back into Periodic’s product and research roadmap. Every customer deployment surfaces new requirements, new data types, and new problem structures — you are the primary conduit for that intelligence.

You Will Thrive in This Role If You Have

• A strong engineering or physical science background — BS/MS/PhD in materials science, chemical engineering, mechanical engineering, physics, or a closely related field. You need to be able to walk into a semiconductor fab or an aerospace R&D lab and talk to the engineers as a peer.

• Hands-on software engineering ability. You can build data pipelines, write clean Python, work with APIs and databases, and integrate systems under time pressure. This is not a purely scientific or purely customer-facing role — it requires both.

• Experience working with messy, real-world industrial or scientific data: process logs, equipment telemetry, metrology outputs, experimental records. You know how to find signal in noise and how to build the infrastructure that keeps that signal clean.

• Proven ability to work directly with technical customers or stakeholders — understanding their constraints, earning their trust, and translating complex technical capabilities into solutions they can actually use.

• Comfort operating in high-ambiguity, high-ownership situations. You will not have perfect tools or complete instructions. You will need to figure things out, build what doesn’t exist, and stay calm when the customer’s data doesn’t look like anything you’ve seen before.

• Willingness to travel regularly and work on-site at customer facilities. Some of those facilities will be unusual environments — cleanrooms, factory floors, high-security industrial sites. This is a feature, not a bug.

Especially Strong Candidates May Also Have

• Experience in a forward-deployed, field engineering, or embedded customer role at an industrial AI company — where the product was complex, the customers were demanding, and deployment meant real production, not demo environments.

• Domain depth in one of Periodic’s core customer verticals: semiconductor process engineering, advanced packaging, aerospace structures or propulsion materials, battery or energy materials, or advanced manufacturing.

• Familiarity with AI/ML workflows, particularly as applied to scientific or industrial data: training pipelines, model evaluation, inference deployment, and the practical realities of 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 those systems 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 the IP, data confidentiality, and export control complexities that come with working inside large industrial customers’ proprietary environments.

Mechanics

• Minimum education: Bachelor’s degree or an equivalent combination of education and training or experience

• Location: Our lab is located in Menlo Park and we prefer folks to be located in Menlo Park or San Francisco but can be flexible based on role

• Compensation: The annual compensation range for this role — $300,000-$400,000

• Visa sponsorship: Yes, we sponsor visas and will do everything we can to assist in this process with our legal support.

We’re building a team of the world’s best — the scientists, engineers, and problem-solvers who don’t just follow the frontier, they define it. If you’re driven to bring AI to life in the physical world and make discoveries that have never been made before, you belong here.