4 дня назад
Edge Computing AI Engineer (Edge AI)
100 000 - 105 000$
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Edge Computing AI Engineer (Edge AI): Design, optimize, and deploy machine learning models for resource-constrained mobile, embedded, and accelerator-based edge devices with an accent on model compression, quantization, hardware-aware optimization, and production deployment. Focus on profiling performance, optimizing compute, memory, energy, and connectivity trade-offs, and building reliable on-device AI with privacy and security considerations.
Location: 100% remote within the United States
Salary: $100,000–$105,000 annually
Company
is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States.
What you will do
- Design, optimize, and deploy machine learning models for resource-constrained edge devices.
- Run AI workloads on mobile platforms, embedded systems, and specialized accelerators.
- Apply model compression, quantization, pruning, and hardware-aware optimization techniques.
- Deploy and maintain production ML models on mobile and embedded platforms.
- Profile and improve performance under compute, memory, energy, and connectivity constraints.
- Address on-device privacy and security considerations while collaborating across functions.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related field.
- 6+ years of ML engineering experience, including significant edge or mobile AI work.
- Strong proficiency in Python and C++.
- Hands-on experience with model compression, quantization, and pruning.
- Experience with at least one major edge inference framework and solid knowledge of mobile and embedded hardware architectures.
- U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates may apply; new H-1B visa petitions cannot be sponsored.
Nice to have
- Experience with custom NPU or DSP toolchains.
- Familiarity with federated learning or on-device personalization.
- Experience with safety-critical or industrial edge deployments.
- Open-source contributions to edge AI frameworks.
- Experience optimizing LLMs for on-device inference.
Culture & Benefits
- Full-time direct W2 employment.
- Fully remote work within the United States.
- Career growth opportunities within an established organization.
- Equal employment opportunity and a workplace free from harassment and discrimination.
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