2 months ago
Beijing, ChinaMid Level
Responsibilities
- Analyze customer needs and co-develop accelerated computing solutions with key enterprise customers.
- Support industry accounts and contribute to research, technical influence, and new business opportunities.
- Deliver technical projects, demonstrations, and client support activities.
- Analyze AI lab workloads involving LLM training and inference acceleration, RAG, agentic AI, kernel analysis, and application optimization.
- Help customers onboard NVIDIA software and hardware products, including CUDA, TensorRT-LLM, and NeMo Framework.
- Serve as a technical thought leader integrating NVIDIA technology into deep learning, data analytics, robotics, signal processing, and other applications.
- Champion data analytics, machine learning, and cybersecurity topics within NVIDIA’s technical community.
Requirements
- 3+ years of experience researching, developing, or applying machine learning, data analytics, or computer vision workflows.
- Strong verbal and written communication skills and the ability to work independently with minimal direction.
- Knowledge of AI and large-model industry applications, trends, and technology stacks.
- Familiarity with large-model inference and training optimization methods.
- Programming experience with C, C++, or Python.
- Experience using scale-out cloud or HPC architectures for parallel programming.
- MS or PhD in engineering, mathematics, physics, computer science, data science, neuroscience, experimental psychology, or a related field, or equivalent experience.
- AIGC, LLM, or NLP experience is preferred.
- CUDA optimization and deep learning framework experience are preferred.
- Experience with model acceleration, kernel optimization, and designing or deploying large-scale HPC or enterprise computing systems is preferred.
Benefits
- Competitive salaries and a generous benefits package.
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.