# Embedded AI Engineer, On-Device Models

Hiring organization: [Deepgram](https://career.thegoodapps.co/organizations/deepgram)

Canonical page: https://career.thegoodapps.co/jobs/577b48c3-e8ce-4314-b6ba-e7ebff83d12c

Listed on Deepgram's own careers site. Applications go to them directly.

- Seniority: Senior
- Location: USA | Remote
- Remote: yes
- Salary: 219300 – 274100 USD per year

## Summary

This role focuses on adapting Deepgram's speech AI models to run efficiently on resource-constrained devices like phones, wearables, and edge hardware. You'll optimize models for low power and memory through techniques like quantization and pruning, write performance-critical embedded code, and integrate with hardware accelerators to bring real-time voice AI to consumer devices.

_Our summary, not Deepgram's wording._

## Skills named

C++, Embedded Linux, Model Compression & Quantization, Rust

## Required

- Production experience on resource-constrained embedded systems, mobile, or edge AI
- Strong C, C++, or Rust proficiency for performance-critical constrained code
- Hands-on model optimization for on-device deployment including quantization or pruning
- Familiarity with edge inference runtimes like ONNX Runtime, TensorRT, or TFLite
- Understanding of CPU, GPU, NPU, DSP architectures and memory hierarchies
- Experience in bare-metal or RTOS environments such as FreeRTOS or Zephyr
- Strong communication and ability to scope and drive optimization problems to measurable results

## Nice to have

- Real-time audio processing on embedded platforms or wake-word detection
- Depth in ML optimization techniques like mixed-precision inference or neural architecture search
- Hardware evaluation and benchmarking experience across accelerators and SoCs
- Shipped AI features in consumer products at scale
- Experience with model compilation toolchain tradeoffs across hardware targets
- Secure on-device deployment practices including code signing and encrypted model storage

Apply on Deepgram's site: https://jobs.ashbyhq.com/Deepgram/0b7494b3-a91e-4540-9439-5b10a1e5b391/application