Role OverviewWe are looking for an 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.
What You Will Do
Design and implement edge AI solutions, apply quantization and pruning techniques, tune model performance for latency and energy efficiency, build cross-platform inference runtimes, and implement on-device model update and versioning workflows.
Why It Might Be a Fit
The ideal candidate has shipped edge AI in production environments where compute, memory, energy, and connectivity constraints fundamentally shape the engineering trade-offs. The role requires strong communication and cross-functional collaboration skills, and experience with on-device privacy and security considerations.
Requirements
- 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.
Benefits
- Salary Range: $100,000–$155,000 Annually
- 100% Remote (U.S.)
- Full-time, Direct W2
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