
ML Engineer — Aerospace Applications
Advance real‑time aerodynamic analytics with deep learning, filtering, and sensor fusion.
Nice to have: PyTorch, JR3/DAQ, control theory.
ApplyWe’re building the future of aerodynamic intelligence. If you love AI, sensors, and making labs smarter, this is your runway.
Remote‑friendly. We welcome applicants from universities, labs, and industry.

Advance real‑time aerodynamic analytics with deep learning, filtering, and sensor fusion.
Nice to have: PyTorch, JR3/DAQ, control theory.
Apply
Build plug‑and‑play sensor stacks for university and research wind tunnels; travel optional.
Nice to have: PLC/DAQ, safety interlocks, Python.
ApplyShip scalable APIs and dashboards for live aerodynamic coefficients and diagnostics.
Nice to have: FastAPI/Flask, TypeScript, Plotly.
Apply
Support faculty and students adopting Aeronex toolkits; craft workshops and demos.
Nice to have: Lab TA experience, customer success.
Apply
Create lab guides, calibration playbooks, and onboarding videos for our platform.
Nice to have: Markdown/Docs, video editing, pedagogy.
Apply
Contribute to tests, dashboards, and data pipelines. Flexible schedules for students.
ApplyWe invest in people so you can do your best work.
Structured, respectful, and fast.
How we work together, every day.
Yes—most roles are remote‑friendly with core hours overlapping U.S. Central Time for meetings.
Absolutely. We design internships and part‑time roles around academic schedules.
Not always. We value strong fundamentals in ML, controls, data, or systems engineering.
CV/resume, GitHub or portfolio links, and a short note about why Aeronex.