4 дня назад
AI Systems Engineer (Edge AI)
100 000 - 150 000$
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
AI Systems Engineer (Edge AI): Designing, optimizing, and deploying machine learning models for mobile, embedded, and specialized accelerator platforms with an accent on model compression, quantization, and hardware-aware optimization. Focus on profiling performance, shipping production AI under compute, memory, energy, and connectivity constraints, and implementing on-device privacy and security.
Location: 100% remote within the United States
Salary: $100,000–$150,000 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 accelerators.
- Apply model compression, quantization, pruning, and hardware-aware optimization techniques.
- Deploy reliable AI capabilities to production on mobile and embedded platforms.
- Profile and improve performance while accounting for compute, memory, energy, and connectivity constraints.
- Address on-device privacy and security considerations.
- Collaborate with cross-functional stakeholders and communicate technical decisions clearly.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related field.
- 6+ years of ML 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.
- Solid understanding of mobile and embedded hardware architectures, production deployment, performance engineering, and profiling.
- Must be authorized to work in the United States; 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.
- Exposure to 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.
- Remote work within the United States.
- Career growth opportunity within an established technology organization.
- Equal employment opportunity workplace.
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