AI engineer
4 days ago
Lenovo is a US$57 billion revenue global technology powerhouse, ranked #248 in the Fortune Global 500, and serving millions of customers every day in 180 markets. Focused on a bold vision to deliver Smarter Technology for All, Lenovo has built on its success as the world’s largest PC company with a full-stack portfolio of AI-enabled, AI-ready, and AI-optimized devices (PCs, workstations, smartphones, tablets), infrastructure (server, storage, edge, high performance computing and software defined infrastructure), software, solutions, and services. Lenovo’s continued investment in world-changing innovation is building a more equitable, trustworthy, and smarter future for everyone, everywhere. Lenovo is listed on the Hong Kong stock exchange under Lenovo Group Limited (HKSE: 992) (ADR: LNVGY).
This transformation together with Lenovo’s world-changing innovation is building a more inclusive, trustworthy, and smarter future for everyone, everywhere. com , and read about the latest news via our StoryHub .
Description and RequirementsAI Performance engineer will collaborate both internally and externally to identify and enable leadership features and performance for our customers across Lenovo’s AI PC client portfolio.
- Advising product Segments about the distinctions among AI solutions from our suppliers and enabling client-based AI applications
- Determining appropriate analysis processes to identify tradeoffs between cost, performance, and battery life for various client-based AI workloads
- Develop expertise with industry-standard benchmarks for on-device AI, as well as monitor, and influence where appropriate industry efforts for on-device AI benchmarking tools
- Be a resource for segment marketing and sales teams as they promote AI PC products
- Initiate and contribute to innovation proof of concepts to leverage on-board AI
- Bachelor degree in Computer Science, Electrical Engineering, or a related field
- 1+ year of experience in a related field
- Passion for software performance optimization, getting the best out of hardware andshaping the future AI performance
- Understanding of CPU and GPU architectures and low-level optimization techniques, memory hierarchy, instruction scheduling, and performance tradeoffs
- Understanding of AI training workloads and demands they exert on networks.
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