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.

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.

• 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.

• 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 models 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.

• 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 hardware.
• Fluency in the modern scientific-Python and ML-systems stack (PyTorch/JAX async services job queues vector or time-series databases).
• Understanding of model-scaling principles and their practical trade-offs.

You will do real research and apply it to a product: rigorous tested and trustworthy because people will fly behind it.