AI role · Open offer

ML Engineer — edge & on-device inference

Front Range Edge AI

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At a glance

How you'll work, where, and key listing dates.

Employment: Full-time
Workplace: Hybrid
Location: Denver, CO, United States · Hybrid
Compensation: $145k–$185k + equity
Experience level: Senior
Team: Edge ML
Benefits & perks

About this role

What the team is looking for and how this position fits.

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.

Skills we're hiring for

Core capabilities—LLMs, automation, analytics, product, or domain expertise.

PythonTensorFlowPyTorch

Tools & stack

Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).

Docker

You're a strong fit if…

You've shipped real work with AI—not only experimented in isolation.
Several listed skills or tools show up in your recent projects.
You can explain trade-offs (quality, cost, latency, safety) in plain language.

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What the employer asked for

Latency or size budget you improved measurably.

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