Applied ML Engineer - Edge Devices
Deepgram · Engineering
- Location
- Remote, California, United States
- Workplace
- Remote
- Level
- Unspecified
- Type
- Full-time
- Pay
- $155K–$245K/yr (published by employer)
- Posted
- 1 month 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 today we run them at scale on NVIDIA GPUs. Our customers increasingly need those same models on hardware we don't control: non-NVIDIA accelerators, edge servers, and embedded…
What you'll do
Port Deepgram speech models to non-NVIDIA and edge platforms, adapting model structure and parameters so they run within the target's existing operator set, runtime, and kernels with minimal modification. Own serving-side model decisions for edge targets:…
You'll love this role if you
Have already fought to get a model running on hardware that wasn't built for it, and want to do that across many platforms. Prefer changing the model to fit the hardware over changing the hardware to fit the model, and know when each is the right call. Care…
It's important to us that you have
Hands-on experience deploying ML models to edge or non-NVIDIA hardware in production. This is required. Cloud-only or GPU-only serving experience does not qualify on its own. Working knowledge of quantization and precision tradeoffs (INT8, FP16, mixed…
It would be great if you had
Experience with speech, audio, or streaming/real-time models specifically. Exposure to writing or reading low-level kernels (CUDA, Metal, NEON, or vendor DSP code), enough to collaborate closely with embedded engineers. Experience with model security or…
Excerpts only. The employer’s page has the complete posting.