2 months ago
Shanghai, China or Beijing, ChinaMid Level
Responsibilities
- Analyze customer needs and co-develop accelerated computing solutions with financial-services customers.
- Support industry accounts, research, technical influence, and new business initiatives.
- Deliver technical projects, demonstrations, and client support activities.
- Analyze financial workloads including quant algorithms, portfolio optimization, trading algorithms, LLM training and inference, agentic AI/RAG applications, and kernel optimization.
- Help customers onboard NVIDIA software and hardware products, including CUDA, CUDA-X, and related libraries.
- Advise on integrating NVIDIA technology into deep learning, high-performance data analytics, and agentic AI applications.
- Promote data analytics, machine learning, and deep learning within NVIDIA’s technical community.
Requirements
- At least 3 years of experience in machine learning, data analytics, or HPC workflow research, development, or application.
- Strong verbal and written communication skills and the ability to work independently.
- Knowledge of AI and large-model applications and trends in the financial-services industry.
- Familiarity with financial technology stacks and quant workflow optimization methods.
- Programming experience in C, C++, and Python.
- Experience with scale-out cloud or HPC architectures for parallel programming.
- MS or PhD in engineering, mathematics, physics, computer science, data science, neuroscience, or experimental psychology, or equivalent experience.
- Preferred experience with algorithmic trading pipelines, LLM/agent/harness systems in finance, model acceleration, kernel optimization, and large-scale HPC or enterprise computing system design and deployment.
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.