Deepgram - Embedded AI Engineer, On-Device Models
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. • 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.
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. • 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.
Benefits
• Estimated Base Salary $219.3K – $274.1K • Offers Equity • Offers Bonus • 10% Annual Bonus • This range is determined by work location and additional factors, including job-related skills and experience. There may be instances where a salary higher or lower than this range may be appropriate for a candidate whose qualifications differ meaningfully from those listed in the job description. Please note that the compensation details listed on US role postings reflect the base salary only and does not include bonus, equity or benefits. • Upload your resume here to autofill key application fields. • Drop your resume here! • Parsing your resume. Autofilling key fields... • Please Note: we have set up limits for applications for this role. It is in the General Application Limit Setting group. The following limits apply to applications for all jobs within this group: • General Application Limit Setting • Candidates may not apply more than 2 times in any 60 day span for any job in the General Application Limit Setting Group. • Candidates may not re-apply to the same role within 180 days if not presented with an offer • or drag and drop here
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