Compute Architect jobs at Nvidia in China, Shanghai

What you’ll be doing:

• Analyze state of the art DL networks (LLM etc.), identify and prototype performance opportunities to influence SW and Architecture team for NVIDIA's current and next gen inference products.

• Develop analytical models for the state of the art deep learning networks and algorithm to innovate processor and system architectures design for performance and efficiency.

• Specify hardware/software configurations and metrics to analyze performance, power, and accuracy in existing and future uni-processor and multiprocessor configurations.

• Collaborate across the company to guide the direction of next-gen deep learning HW/SW by working with architecture, software, and product teams.

What we need to see:

• BS or higher degree in a relevant technical field (CS, EE, CE, Math, etc.).

• Strong programming skills in Python, C, C++.

• Strong background in computer architecture.

• Experience with performance modeling, architecture simulation, profiling, and analysis.

• Prior experience with LLM or generative AI algorithms.

Ways to stand out from the crowd:

• GPU Computing and parallel programming models such as CUDA and OpenCL.

• Architecture of or workload analysis on other deep learning accelerators.

• Deep neural network training, inference and optimization in leading frameworks (e.g. Pytorch, TensorRT-LLM, vLLM, etc.).

• Open-sourceAIcompilers (OpenAI Triton, MLIR, TVM, XLA, etc.).

What you will be doing:

• Develop and optimize the control stack, including locomotion, manipulation, and whole-body control algorithms;

• Deploy and evaluate neural network models in physics simulation and on real humanoid hardware;

• Design and maintain teleoperation software for controlling humanoid robots with low latency and high precision;

• Implement tools and processes for regular robot maintenance, diagnostics, and troubleshooting to ensure system reliability;

• Monitor teleoperators at the lab and develop quality assurance workflows to ensure high-quality data collection;

• Collaborate with researchers on model training, data processing, and MLOps lifecycle.

What we need to see:

• Bachelor’s degree in Computer Science, Robotics, Engineering, or a related field;

• 3+ years of full-time industry experience in robotics hardware or software full-stack;

• Hands-on experience with deploying and debugging neural network models on robotic hardware;

• Ability to implement real-time control algorithms, teleoperation stack, and sensor fusion;

• Proficiency in languages such as Python, C++, and experience with robotics frames (ROS) and physics simulation (Gazebo, Mujoco, Isaac, etc.).

• Experience in maintaining and troubleshooting robotic systems, including mechanical, electrical, and software components.

• Physically work on-site on all business days.

Ways to stand out from the crowd:

• Master’s or PhD’s degree in Computer Science, Robotics, Engineering, or a related field;

• Experience at humanoid robotics companies on real hardware deployment;

• Experience in robot hardware design;

• Demonstrated Tech Lead experience, coordinating a team of robotics engineers and driving projects from conception to deployment.

What you’ll be doing:

• Analyze state of the art DL networks (LLM etc.), identify and prototype performance opportunities to influence SW and Architecture team for NVIDIA's current and next gen inference products.

• Develop analytical models for the state of the art deep learning networks and algorithm to innovate processor and system architectures design for performance and efficiency.

• Specify hardware/software configurations and metrics to analyze performance, power, and accuracy in existing and future uni-processor and multiprocessor configurations.

• Collaborate across the company to guide the direction of next-gen deep learning HW/SW by working with architecture, software, and product teams.

What we need to see:

• BS or higher degree in a relevant technical field (CS, EE, CE, Math, etc.).

• Strong programming skills in Python, C, C++.

• Strong background in computer architecture.

• Experience with performance modeling, architecture simulation, profiling, and analysis.

• Prior experience with LLM or generative AI algorithms.

Ways to stand out from the crowd:

• GPU Computing and parallel programming models such as CUDA and OpenCL.

• Architecture of or workload analysis on other deep learning accelerators.

• Deep neural network training, inference and optimization in leading frameworks (e.g. Pytorch, TensorRT-LLM, vLLM, etc.).

• Open-sourceAIcompilers (OpenAI Triton, MLIR, TVM, XLA, etc.).

What you’ll be doing:

• Explore innovative GPU composition and novel system functionalities related to processing and storage.

• Connect with GPU architecture designers to make advised and random functional testing plan which provides good coverage.

• Innovatively develop and improve infrastructure and methodology to generate tests.

• Massively generate, run and debug tests in various platforms, e.g., functional simulator, full chip and unit-level RTL, emulator and silicon.

• Build innovative tools to improve efficiency.

What we need to see:

• Knowledge of computer architecture, compiler, assembly language.

• Good understanding of GPU concept and pipeline, in terms of parallel computing and/or memory system.

• Familiar with Linux

• Good at C++ and Python development.

• Bachelor in CS or EE. MS or PhD or equivalent experience is a plus.

Ways to stand out from the crowd:

• Knowledge and experience of CUDA programming and debugging.

• Random system development and work experience.

• ASIC experience.

• Compiler experience

What you’ll be doing:

• Develop highly optimized deep learning kernels for inference

• Do performance optimization, analysis, and tuning

• Work with cross-collaborative teams across automotive, image understanding, and speech understanding to develop innovative solutions

• Occasionally travel to conferences and customers for technical consultation and training

• Masters or PhD or equivalent experience in relevant discipline (CE, CS&E, CS, AI)

• SW Agile skills helpful

• Excellent C/C++ programming and software design skills

• Python experience a plus

• Performance modelling, profiling, debug, and code optimization or architectural knowledge of CPU and GPU

• GPU programming experience (CUDA or OpenCL) desired

• 5 years of relevant work experience

• Design and develop the architecture, interface and features of the GPU kernel library

• Keep improving the quality and performance of the library and its GPU kernels

• Explore and expand the boundary of innovative technologies like GPU code generation and fusion

• Contribute to NVIDIA's AI business by collaborating closely with DL product teams as well as kernel development teams

• MS, PhD or equivalent in relevant fields (CS, EE, Math)

• 2+ years of relevant work or research experience

• Strong programming skills in C, C++, and Python

• Excellent problem solving skills and learning capability

• Experience with designing software architecture, interfaces, and building testing infrastructures

• Good communication and a great teammate

• Familiar with CUDA programming and GPU architecture

• Background with DL fundamentals, frameworks, graph compilers, LLVM, MLIR etc.

• Hands-on experience in development on Linux and Windows platforms, C++ build tools like CMake and DevOps tools, including Docker, Jenkins, Kubernetes etc.

• Track record of mentoring junior engineers and leading a project and a team