英伟达Software Engineer, LLM Inference
任职要求
• Masters or higher degree in Computer Engineering, Computer Science, Applied Mathematics or related computing focused degree (or equivalent experience) • 3+ years of relevant software development experience. • Excellent C/C++ programming and software design skills, including debugging, performance analysis, and test design. • Strong curiosity about artificial intelligence, awareness of the latest developments in deep learning like LLMs, generative models • Experience working with deep learning frameworks like PyTorch • Proactive and able to work without supervision • Excellent written and oral communication skills in English • Strong customer communication skills, powerfully motivated to provide highly responsive support as needed #deeplearning
工作职责
NVIDIA has continuously reinvented itself over two decades. NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world.This is our life’s work — to amplify human imagination and intelligence. AI becomes more and more important in Auto Driving and AI City. NVIDIA is at the forefront of the Auto Driving and AI City revolution and providing powerful solutions for them. All these solutions are based on GPU-accelerated libraries, such as CUDA, TensorRT and V/LLM inference framework etc. Now, we are now looking for an LLM inference framework developer engineer based in Shanghai. What you’ll be doing :• Craft and develop robust inferencing software that can be scaled to multiple platforms for functionality and performance • Performance analysis, optimization and tuning • Closely follow academic developments in the field of artificial intelligence and feature update • Collaborate across the company to guide the direction of machine learning inferencing, working with software, research and product teams
• You will develop and optimize software solutions to accelerate LLM inference using GPU technology. • Collaborate closely with a world-class team of engineers to implement and refine GPU-based algorithms. • Analyze and determine the most effective methods to improve performance, ensuring seamless execution across diverse computing environments. • Engage in both individual and team projects, contributing to NVIDIA's mission of leading the AI revolution. • Work in an empowering and inclusive environment to successfully implement groundbreaking AI solutions.
- Keep up to date with and utilize the latest developments in LLM system optimization.- Take the lead in designing innovative system optimization solutions for internal LLM workloads.- Optimize LLM inference workloads through innovative kernel, algorithm, scheduling, and parallelization technologies.- Continuously develop and maintain internal LLM inference infrastructure.- Discover new LLM system optimization needs and innovations.
- Keep up to date with and utilize the latest developments in LLM system optimization.- Discover/solve impactful technical problems, advance state-of-the-art LLM technologies, and translate ideas into production.- Optimize LLM inference workloads through innovative kernel, algorithm, scheduling, and parallelization technologies.- Continuously maintain internal LLM inference infrastructure.
• Design, build, and optimize containerized inference execution for LLM applications, ensuring efficiency and scalability. These applications may run in container orchestration platforms like Kubernetes to enable scalable and robust deployment. • Ensure the performance and scalability of NIMs through comprehensive performance measurement and optimization. • Apply container expertise to create and optimize the basic building blocks of NIMs, influencing the development of many models and related products within NVIDIA. • Collaborate, brainstorm, and improve the designs of inference solutions and APIs with a broad team of software engineers, researchers, SREs, and product management. • Mentor and collaborate with team members and other teams to foster growth and development. Demonstrate a history of learning and enhancing both personal skills and those of colleagues.