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Physical AI Engineer - SW

3 days, 9 hours ago
United States
Full-time
Mid Level
AI Engineer
Software Development
Git Machine Learning Python PyTorch
Apply Now
Archer

Archer

Archer Technologies provides enterprise governance, risk, and compliance solutions through the Archer SmartSuite framework. Additionally, Archer is designing and developing electric vertical takeoff and landing aircraft for urban air mobility networks....

Construction & Engineering
251-1K
Founded 2018
View All Jobs 30

Description

  • Build, train, and validate machine-learning models that approximate physical systems with calibrated uncertainty.
  • Generate and curate synthetic datasets by pairing parametric geometry with high-fidelity physics solves.
  • Develop learned models that complement traditional CFD, FEA, and optimization solvers.
  • Integrate foundation models into agentic engineering workflows that orchestrate, route, and draft work.
  • Build ML systems with reliable, traceable outputs that engineers can trust and verify.
  • Take research prototypes into production as typed, tested Python code with reproducible workflows.
  • Partner with aerodynamics, structures, propulsion, GN&C, and avionics teams to automate engineering analyses.
  • Compare model predictions against test-rig and flight data and improve models based on results.

Requirements

  • Strong Python programming fundamentals with experience building and scaling ML or data pipelines in a version-controlled codebase.
  • Hands-on machine learning experience, including training, evaluating, and debugging models.
  • Experience taking a research idea into a working, tested implementation.
  • Working knowledge of scientific machine learning, such as physics-informed models, neural operators, or surrogate modeling, or a strong applied-math, numerical-methods, or simulation background.
  • Experience generating or working with synthetic data for model training.
  • Experience integrating foundation models into software and understanding when verified computation or human judgment is required.
  • BSc, MSc, or equivalent experience in a quantitative or engineering discipline such as computer science, applied math, mechanical/aerospace engineering, or physics.
  • Solid command of Git and modern software development best practices.
  • Strong communication skills and the ability to collaborate across software, hardware, and engineering disciplines.
  • Interest in aviation and building learning systems that hold up under real-world scrutiny.
  • Background in aerospace, mechanical, or physical sciences; familiarity with CFD, FEA, or MDAO.
  • Experience with differentiable optimization, constrained learning, or enforcing physical constraints in learned models.
  • Exposure to safety-critical or regulated-systems environments.
  • Sim-to-real experience, including domain randomization, system identification, or reconciliation against hardware or flight-test data.
  • Hands-on lab instrumentation experience, including oscilloscopes, logic analyzers, protocol analyzers, or HIL/SIL rigs.
  • Fluency with PyTorch, JAX, async services, job queues, vector databases, or time-series databases.

Benefits

  • Base salary target of $144,000 to $180,000.
  • Pay-for-performance compensation culture.
  • Reasonable accommodations for applicants with disabilities or sincerely held religious beliefs.
  • Equal Opportunity employer with a strong commitment to diversity and inclusion.
  • No work visa sponsorship available for this role at present.

Interested in this position?

Apply directly on the company website

Apply Now

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