Deepgram

Embedded AI Engineer, On-Device Models

USA | Remote

Sponsorship not specifiedDetected 14 days ago

C++LinuxMachine LearningEmbedded SystemsSignal ProcessingResearchCommunication

About the role

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.

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.
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.
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.

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 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.
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.

This listing is sourced directly from Deepgram's careers page and normalized into a canonical job model.