Embedded AI Engineer, On-Device Models

Deepgram•Remote, CA

1d•$219,300 - $274,100•Remote

About The Position

Deepgram's speech AI models are among the fastest and most accurate in the world — and the next wave of voice experiences won't live only in the cloud. They'll run directly on the small, low-power devices people carry, wear, and keep around their homes: phones, earbuds, wearables, appliances, cameras, and purpose-built consumer hardware. Putting state-of-the-art speech models on devices with tight memory, compute, thermal, and battery budgets is a fundamentally different engineering problem, and it's one of the most important frontiers for bringing voice AI to everyone. As an Embedded AI Engineer, you will take Deepgram's models and make them run — fast, accurately, and efficiently — on resource-constrained embedded and edge platforms. You'll work across the stack: optimizing and compiling models for on-device inference, writing performance-critical runtime code, and squeezing every last millisecond and milliwatt out of a wide range of mobile application processors, embedded SoCs, microcontrollers, and dedicated AI accelerators. Your work directly enables a new class of private, offline-capable, real-time voice experiences on the devices closest to the user. This role is a great fit whether you're a hands-on senior embedded engineer who wants to go deep on a hard problem, or a staff-level technical leader who wants to define how Deepgram's voice AI gets onto consumer hardware and raise the bar for the engineers around you. We'll set the level to your experience.

Requirements

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 environments.
Hands-on experience with model optimization for on-device deployment, including quantization, pruning, knowledge distillation, or architecture-specific compilation.
Familiarity with edge inference runtimes (e.g., ONNX Runtime, TensorRT, TFLite, ExecuTorch) and/or vendor-specific NPU/DSP toolchains.
A strong understanding of hardware-software interaction — CPU/GPU/NPU/DSP architectures, memory hierarchies, fixed-point/integer arithmetic, and power management — and how they affect inference performance.
Experience working close to the metal: bare-metal or RTOS environments (e.g., FreeRTOS, Zephyr), embedded Linux, or microcontroller and edge SoC development.
Strong communication skills and a builder mindset — you can scope an ambiguous optimization problem, drive it to a measurable result, and explain the tradeoffs clearly.

Nice To Haves

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 schemes, mixed-precision inference, or neural architecture search for edge targets.
Background in hardware evaluation and benchmarking — systematically comparing accelerators, SoCs, or GPUs for specific workload profiles.
Experience shipping AI features in consumer products at scale, and the instinct for what "production quality" means on a battery-powered device.
Familiarity with model compilation and optimization toolchains and their tradeoffs across hardware targets.
Experience with secure, robust on-device deployment practices — code signing, encrypted model storage, and safe update mechanisms.

Responsibilities

Take Deepgram's Speech and Conversational models and get them running on embedded and low-power consumer hardware — defining the architecture for on-device, real-time inference across a diverse range of processors and accelerators.
Optimize models for constrained targets through quantization, pruning, distillation, operator fusion, and architecture-specific compilation to meet strict latency, memory, power, and thermal budgets.
Write and optimize performance-critical runtime code (C, C++, and/or Rust) for embedded environments, including bare-metal and real-time operating systems such as FreeRTOS and Zephyr.
Integrate with industry-standard edge inference runtimes and vendor NPU/DSP toolchains, mapping model graphs efficiently onto on-device accelerators and CPU/GPU/NPU heterogeneity.
Build the on-device runtime plumbing: model packaging, deployment pipelines, over-the-air update mechanisms, and lightweight telemetry for devices operating with limited or intermittent connectivity.
Establish repeatable benchmarking and validation across target hardware — measuring latency, accuracy, power consumption, memory footprint, and resource utilization — and catch regressions before they ship.
Partner with silicon and device vendors on SDK integration and performance tuning, getting our models to run efficiently on new chipsets and reference platforms.
Collaborate with Research and Engine teams to influence model architectures toward edge-friendly designs from the start, reducing the optimization burden at deployment time.