Edge AI Engineer
$100,000–$150,000 year
Remote · United States
Job Summary
Edge AI Engineer to design, optimize, and deploy machine learning models that run efficiently on resource-constrained edge devices, including mobile platforms, embedded systems, and specialized accelerators. The role requires deep expertise in model compression, quantization, and hardware-aware optimization, along with strong systems engineering skills to ship reliable AI capabilities outside the data center. The ideal candidate has shipped edge AI in production environments where compute, memory, energy, and connectivity constraints fundamentally shape the engineering trade-offs. Responsibilities include designing and implementing edge AI solutions for mobile SoCs, NPUs, and embedded accelerators; applying quantization, pruning, distillation, and architectural optimization; tuning performance for latency and energy on target hardware; building cross-platform inference runtimes (TensorFlow Lite, ONNX Runtime, Core ML); optimizing for accelerator backends; implementing on-device updates, versioning, and rollback workflows; designing hybrid edge-cloud architectures; building privacy-conscious telemetry pipelines; collaborating with hardware/firmware/product teams; ensuring secure execution and model integrity on edge devices; developing benchmarking suites; driving responsible AI considerations; maintaining comprehensive technical documentation; and staying current with edge AI developments and translating advances into actionable recommendations.
Required Qualifications
- Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related field.
- Six or more years of experience in ML engineering, with significant work on edge or mobile AI.
- Strong proficiency in Python and C++.
- Hands-on experience with model compression, quantization, and pruning techniques.
- Experience with at least one major edge inference framework.
- Solid understanding of mobile and embedded hardware architectures.
- Experience deploying ML models to production on mobile or embedded platforms.
- Strong performance engineering and profiling skills.
- Familiarity with on-device privacy and security considerations.
- Strong communication and cross-functional collaboration skills.
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