Embedded AI Engineer, On-Device Models
Deepgram · Engineering
- Location
- Remote, California, United States
- Workplace
- Remote
- Level
- Unspecified
- Type
- Full-time
- Pay
- $219K–$274K/yr (published by employer)
- Posted
- 4 months ago
- Source
- Ashby
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What the posting covers
Company Overview
Deepgram is the leading platform underpinning the emerging trillion-dollar Voice AI economy, providing real-time APIs for speech-to-text (STT), text-to-speech (TTS), and building production-grade voice agents at scale. More than 200,000 developers and 1,300+…
Company Operating Rhythm
At Deepgram, we expect an AI-first mindset—AI use and comfort aren’t optional, they’re core to how we operate, innovate, and measure performance. Every team member who works at Deepgram is expected to actively use and experiment with advanced AI tools, and…
About the Role
Deepgram's speech models are among the fastest and most accurate in the world, and we have deep machinery for running them on NVIDIA GPUs. Our customers need them on everything else: non-NVIDIA accelerators, embedded SoCs, mobile application processors, DSPs…
What You'll Do
Write and optimize custom kernels and operators (C, C++, Rust, and platform assembly or intrinsics) for non-NVIDIA accelerators, embedded SoCs, DSPs, and NPUs where the vendor's standard operator set is insufficient for Deepgram models. Own target-side…
You'll Love This Role If You
Find deep satisfaction in making a large model run on hardware that was never meant to run it, and still hitting accuracy and latency targets. Reach for the profiler and the ISA manual before you reach for a bigger chip. Would rather write the kernel than…
It's Important To Us That You Have
Experience delivering production systems on resource-constrained hardware — embedded systems, mobile, edge AI, or small low-power devices. Strong proficiency in C, C++, and/or Rust, with experience writing performance-critical code for constrained…
It Would Be Great if You Had
Experience with real-time audio processing on embedded platforms — DSP pipelines, audio codec optimization, wake-word or always-on listening, or streaming inference on microcontrollers and edge SoCs. Depth in ML optimization techniques — custom quantization…
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