Embedded AI Engineer, On-Device Models

Deepgram

Embedded AI Engineer optimizing Deepgram's voice AI models for low-power embedded hardware. Collaborating across the stack to achieve real-time inference in constrained environments.

Posted 7/7/2026full-timeRemote • 🇺🇸 United StatesMid-LevelSenior💰 $219,300 - $274,100 per yearWebsite

Tech Stack

Tools & technologies

LinuxRTOSRust

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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.

Benefits

Comp & perks

Offers Equity
Offers Bonus
10% Annual Bonus

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

Model QuantizationModel PruningKnowledge DistillationArchitecture-Specific CompilationPerformance-Critical Code Optimization

Soft Skills

Strong Communication SkillsBuilder Mindset