Physical AI Engineer - SW

Archer is an aerospace company based in San Jose, California building an all-electric vertical takeoff and landing aircraft with a mission to advance the benefits of sustainable air mobility. We are designing, manufacturing, and operating an all-electric aircraft that can carry four passengers while producing minimal noise.

Our sights are set high and our problems are hard, and we believe that diversity in the workplace is what makes us smarter, drives better insights, and will ultimately lift us all to success. We are dedicated to cultivating an equitable and inclusive environment that embraces our differences, and supports and celebrates all of our team members.

About the Role

Archer is developing electric vertical-takeoff aircraft, and our SW team builds the advanced simulation, machine learning, and engineering tooling that supports how those aircraft are designed and analyzed. We are looking for a Physical AI Engineer who works at the intersection of scientific machine learning, software engineering, and aerospace - building learned models of physical systems and the AI-driven workflows that put them to work.

This is a hands-on research-and-build role. You will train models that approximate expensive physics, integrate foundation models into engineering tooling, and turn promising research into reliable, well-tested software that other engineers depend on.

What You'll Do

* Build, train, and validate machine-learning models that approximate the behavior of physical systems - neural operators, physics-informed networks, and related surrogate models - to evaluate engineering questions far faster than traditional simulation, with calibrated, honest uncertainty.

* Generate and curate large-scale synthetic datasets - parametric geometry paired with high-fidelity physics solves - to train and stress-test those models.

* Build learned models that work alongside traditional CFD/FEA and optimization solvers, so engineers get fast answers without giving up trusted ones.

* Integrate frontier foundation models (e.g., Claude) into agentic engineering workflows, where the model orchestrates, routes, and drafts - and verified computation plus human judgment govern the outcome.

* Build ML systems whose outputs are reliable and traceable, so the results engineers act on can be trusted and checked.

* Take research from paper or prototype to production: ship into a typed, tested Python monorepo with real reproducibility - not one-off notebooks.

* Partner with aerodynamics, structures, propulsion, GN&C, and avionics engineers to turn their analyses into automated, dependable workflows.

* Help connect simulation to reality - comparing model predictions against test-rig and flight data and improving the models from what you learn.

What You Need

* Strong programming fundamentals and excellent Python, with a track record of building and scaling ML or data pipelines inside a real, version-controlled codebase - and the testing discipline and reproducibility that production systems require.

* Hands-on machine learning experience: training, evaluating, and debugging models, and a demonstrated ability to take a research idea to a working, tested implementation.

* Working knowledge of scientific machine learning - physics-informed models, neural operators, or surrogate modeling - or a strong applied-math, numerical-methods, or simulation background and the ability to ramp into it quickly.

* Experience generating or working with synthetic data to train learned systems.

* Sound judgment about foundation models: you have integrated them into software, and you understand where a model can be trusted and where it must be backed by verified computation or a human decision.

* An evidence-first instinct - you treat a model's output as only as good as the data and verification behind it, and you build systems that make that explicit.

* BSc, MSc, or equivalent experience in a quantitative or engineering discipline (computer science, applied math, mechanical/aerospace engineering, physics, or related).

* Solid command of Git and modern software-development best practices.

* Strong communication and the ability to collaborate across software, hardware, and engineering disciplines.

* Genuine interest in aviation and in building learning systems that hold up under real-world scrutiny.

Nice to Haves

* Background in aerospace, mechanical, or a physical-sciences domain; familiarity with CFD, FEA, or multidisciplinary design analysis and optimization (MDAO).

* Experience with differentiable optimization, constrained learning, or enforcing physical constraints inside learned models.

* Exposure to safety-critical or other regulated-systems environments - or a real appetite to learn how they work.

* Sim-to-real techniques (domain randomization, system identification) and experience reconciling models against hardware or flight-test data.

* Hands-on lab instrumentation (oscilloscopes, logic analyzers, protocol analyzers, HIL/SIL rigs) - valuable where the work meets real test…