4 дня назад
Intelligent Edge Engineer
135 000 - 155 000$
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Intelligent Edge Engineer (Machine Learning/Edge AI): Designing, optimizing, and deploying machine learning models for mobile platforms, embedded systems, and specialized accelerators with an accent on model compression, quantization, and hardware-aware optimization. Focus on profiling resource-constrained inference, deploying reliable on-device AI in production, and addressing compute, memory, energy, connectivity, privacy, and security challenges.
Location: 100% remote within the United States
Salary: $135,000–$155,000 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 mobile platforms, embedded systems, and specialized edge accelerators.
- Apply model compression, quantization, pruning, and hardware-aware optimization techniques.
- Deploy machine learning models to production on mobile and embedded platforms.
- Engineer reliable AI capabilities under compute, memory, energy, and connectivity constraints.
- Profile and optimize system performance while collaborating with cross-functional teams.
- Address on-device privacy and security considerations.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related field.
- 6+ years of machine learning engineering experience, including significant work with edge or mobile AI.
- 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 a solid understanding of mobile and embedded hardware architectures.
- Must be eligible to work in the United States as a U.S. citizen, Green Card holder, EAD holder, or H-1B transfer candidate; 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 W-2 employment.
- 100% remote work within the United States.
- Career growth opportunities within an established organization.
- Equal employment opportunity and a workplace free from discrimination and harassment.
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