Periodic Labs

Forward Deployed Engineer, Quantum Simulations

Menlo Park · Exec

H1B sponsorship available$180k-$250kDetected 36 days ago

PythonAWSGCPAzureMachine LearningDesign SystemsProduct ManagementAccount ManagementResearchExperimental Design

About the role

This is a technical and scientific role, not a pure account management role.
You will write code, debug workflows, configure integrations, and surface product gaps back to our internal teams.
You will be the person in the room who understands both the physics of a density functional theory calculation and the API call needed to run it at scale.

Write and maintain integration code, custom scripts, and workflow automation that connect customer simulation environments (VASP, Quantum ESPRESSO, FHI-aims, CP2K, or similar) to Periodic Labs systems
Configure, validate, and troubleshoot high-throughput simulation pipelines on cloud and HPC environments, ensuring accuracy, reproducibility, and performance
Collaborate with internal ML and materials science teams to test new simulation capabilities against customer use cases before release
Develop reusable technical assets: onboarding guides, workflow templates, integration libraries, and best-practice documentation that accelerate future deployments
comfortable scripting automation, parsing simulation outputs, and building lightweight tooling around simulation workflows
Strong programming skills in Python; comfortable scripting automation, parsing simulation outputs, and building lightweight tooling around simulation workflows
Familiarity with the Periodic Labs Onnes platform or similar AI-assisted experimental design systems
We're an AI and physical sciences company building state-of-the-art models to accelerate breakthroughs across materials, energy, and beyond.
Our team brings deep expertise, genuine ownership, and an insatiable drive to push the boundaries of what's scientifically possible.

Comfort operating in ambiguous, fast-moving environments where the product, the science, and the customer need are all evolving simultaneously
Experience with Machine learned interatomic potentials (MLIPs) like MACE or UMA and differentiable MD simulation packages.
Experience with materials databases and frameworks (e.g., the Materials Project, OQMD, AiiDA, or Atomate2 workflow frameworks)
3-7 years of experience working with quantum chemistry or condensed matter simulation codes, either in research or in an applied/industry context
Hands-on experience with at least one major ab initio or DFT code (VASP, Quantum ESPRESSO, FHI-aims, CP2K, GPAW, or equivalent)
Ability to communicate complex simulation concepts clearly to both technical and semi-technical audiences - you can explain exchange-correlation functionals to a physicist and convergence testing to a program manager

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

Prior experience in a forward deployed, solutions engineering, or field CTO role at a deep tech or scientific software company
You will work directly with customers ranging from advanced semiconductor manufacturers to national labs to energy companies - and your work will directly shape how Periodic Labs grows into these markets.
Embed with customers and research partners to understand their quantum simulation workflows - DFT, quantum chemistry, many-body perturbation theory, mesoscopic scale modeling or force-field-based methods - and translate those workflows into solutions built on Periodic Labs infrastructure
Serve as the primary technical contact for a portfolio of customers, owning the relationship from initial deployment through steady-state operation and expansion
Domain depth in a specific application area relevant to Periodic Labs customers: advanced semiconductor packaging, thin-film deposition, superconducting materials, or energy storage

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

This listing is sourced directly from Periodic Labs's careers page and normalized into a canonical job model.