Deepgram
Embedded AI Engineer, On-Device Models
Rol remoto de Engineering con fit claro de ubicación del candidato.
Publicado7 jul 2026
Países elegibles1 país aceptado
Señal de seniorityNivel abierto
Modelo de trabajoRemoto
Ubicaciones aceptadas para candidatos
Estados Unidos
Resumen del rol
Embedded AI Engineer, On-Device Models
Requisitos y responsabilidades
Contenido del rol extraído en secciones para revisar más rápido.
What You'll Do
- 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.
You'll Love This Role If You
- Find deep satisfaction in making a large model run on a tiny device — and still hit accuracy and latency targets.
- Want to work at the intersection of AI and hardware, where optimization isn't optional but existential.
- Are energized by the back-and-forth of getting a model to sing on a new chipset, runtime, or accelerator.
- Believe on-device AI is the next major deployment frontier and want to define how speech AI gets there for consumers.
- Prefer hard, constrained, ship-it problems over open-ended research — you want to see your work running in people's hands.
- Care about the details that don't show up in a cloud benchmark: cold-start time, power draw, thermals, and memory fragmentation.
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 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.
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 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.
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