Embedded AI Engineer, On-Device Models

🕒 vor 25 Tagen

🇺🇸 Vereinigte Staaten – Remote

💵 $219.300 - $274.100 / Jahr

⏰ Vollzeit

🟡 Mittelstufe

🟠 Senior

⚙️ Systemingenieur

🦅 H1B-Visum-Sponsor

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Deepgram

WebsiteLinkedInAlle Stellen

51 - 200 Mitarbeiter

Gegründet 2015

💼 Beratung

🏥 Gesundheitswesen

📦 Logistik

💰 €47.000.000 Series B im 2022-11

Consulting • Healthcare • Logistics

Deepgram ist ein führendes Voice-AI-Unternehmen, das leistungsstarke APIs für Speech-to-Text, Text-to-Speech und Language-Understanding-Anwendungen bereitstellt. Die Plattform ermöglicht es Entwicklerinnen und Entwicklern, anspruchsvolle Voice-AI-Lösungen für Use Cases wie Contact Center, medizinische Transkription, Conversational AI und mehr zu entwickeln. Bekannt für unübertroffene Genauigkeit, Geschwindigkeit und Kosteneffizienz genießt die Technologie von Deepgram das Vertrauen führender Unternehmen und Start-ups weltweit. Mit Echtzeit- und hochgenauer Transkription hilft Deepgram Unternehmen, Erkenntnisse aus Sprachdaten zu gewinnen – ein unverzichtbares Werkzeug, um Sprachinteraktionen zu transformieren.

Beschreibung

• 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.

🎯 Anforderungen

• 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.

🏖️ Vorteile

• Offers Equity • Offers Bonus • 10% Annual Bonus