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Description and Requirements
• Roles and responsibilities
Lenovo Research (LR) is the R&D division of Lenovo and has always been committed to pushing forward the development of information technology, smart devices, and services, keeping delivering cutting-edge technologies that have been built into Lenovo's high-tech products and services.
This position is to work as an NPU inference engineer in a team of PC Innovation and Ecosystem (PCIE) Lab, research industry architecture and develop Lenovo universal inference platform to support all Lenovo models and dNPU hardware.
We are looking for a passionate candidate who is experience in dNPU tools chain and model structure.
Role and Responsibility
- Responsible for the research and development of AI algorithms, including but not limited to deep learning, machine learning, natural language processing and other related algorithms.
- Participate in the requirement analysis and decomposition of the reasoning system and cooperate with the architecture team to gradually improve the hardware and software architecture.
- According to project requirements, familiar with the corresponding algorithm model, and optimize and improve it.
- Responsible for writing and maintaining relevant technical documents
• Key Interaction with:
- Lenovo Research in Beijing - software, system research
- Lenovo Research in Tianjin - software, system research
- Lenovo Research in Shenzhen - hardware development, electronical engineering
Requirements
Must Have - essential
Experience
- MSc or PhD in computer/information science, electronics engineering, or a related field
- 5+ years' R&D experience in model inference
- Excellent problem analysis and solution ability
Skill, Competency
- Basic and advanced computer vision algorithm design
- Written communication skills in business English for efficient communication with team
- Basic English communication skills
- C/C++, Python and other programming languages, with good programming habits and code norms.
- Linux/Windows platform development experience
Good to have - desirable
Experience
- Experience of collaborating with other researchers or engineers in a global work environment
- Experience of solving problems in the inference of various models
Skill, Competency
- Experience with machine learning, deep learning frameworks like ONNX, GGML, OpenVINO, Tensorflow, PyTorch
- Experience with model optimization like model fixed-point quantization, sparse computing
- Experience with OpenCL, DirectML, CUDA code on GPU, NPU or FPGA
- Experience with AI algorithms and models, such as Transformer, CNN, SD, LAM
Additional Locations:
* China - Tianjin - 天津（Tianjin）