Front Range Edge AI is hiring a Senior ML Engineer — edge & on-device inference on the Edge ML team (Hybrid · Denver, United States). This is a hands-on role in applied machine learning—not a generic “AI enthusiast” post.
You will own modeling, evaluation, and production ML delivery end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.
Front Range Edge AI helps hardware and wellness partners ship on-device models with strict battery, thermal, and privacy budgets. We sit at the intersection of quantisation, DSPs, and mobile OS constraints.
Join us in Denver (hybrid) as an ML Engineer focused on edge deployment and metrics.
What you will do
- Profile and optimise inference on ARM SoCs, NPUs, and MCU-class targets using ONNX/TFLite/CoreML stacks.
- Collaborate with firmware teams on sensor fusion and wake-word trade-offs.
- Instrument field telemetry with privacy-preserving aggregates for drift monitoring.
- Support certification efforts (FDA-adjacent, CE) with reproducible benchmarking packets.
- Document build pipelines for multi-OEM SKU matrices.
What we look for
- 4+ years shipping models on constrained devices—not just datacentre ML.
- Strong Python/C++ literacy; familiarity with profiling tools.
- Structured experimentation mindset and meticulous documentation.
- US work authorisation.
Nice to have
- Experience with tinyML compilers or ONNX Runtime mobile optimisations.
- Outdoor/hardware hobbyist empathy for wearable use cases.