Senior Software Architect Humanoid Robotics jobs at Nvidia in China, Shanghai

What you’ll be doing:

• Be responsible for running test cases to validate NVIDIA GPU Communications Libraries (NCCL, NVSHMEM, UCX, GDRCopy, GPUDirect RDMA etc).

• Be responsible to automate test cases and maintain the automation scripts.

• Collaborate with Developer, PM, marketing, and engineering teams on crafting test plan and implementing validation.

• You will assist in the architecture, crafting and implementing of SWQA test frameworks.

• Be responsible for code coverage improvement and code complexity optimization.

What we need to see:

• BS or higher degree in CS/EE/CE or equivalent experience

• 5+ years of relevant experience

• Seasoned software QA or software testing background; test infrastructure and strong analysis skills

• Be proficient in scripting language (Python, Perl, bash)

• Solid experience with AI development tools for test development and automation

• Knowledge of basic networking concepts

• UNIX/Linux experience is required

• Experiences in C/C++ is required

• Ability to work independently and leadership skillsas well as experience in using quality mindset to drive improvements

• Proficient oral and written English

Ways to stand out from the crowd:

• Experience with CUDA programming and NVIDIA GPUs

• Knowledge of high-performance networks like InfiniBand, RoCE,etc

• Experience with CSPs(AWS, Google Cloud, Oracle Cloud Infrastructure, Microsoft Azure), andHPC cluster,slurm, ansible, etc

• Prior experience with virtualization technologies (KVM, HyperV, VMWARE, OpenStack, Docker, Kubernetes)

• Experience with Deep Learning Frameworks such as PyTorch, TensorFlow, etc

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people.

• Design and implement the DSL and the core compiler of tile-aware GPU programming model for emerging GPU architectures

• Continuously innovate and iterate on the core architecture of the compiler to consistently optimize performance

• Investigation of next-generation GPU architectures and provide solutions in the DSL and compiler stack

• Performance analysis on emerging AI/LLM workloads and integrate with AI/ML frameworks

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

• 4 + years of relevant work experience

• Excellent C/C++ programming and software engineering skills, ACM background is a plus

• Good fundamental knowledges on computer architecture

• Strong ability in abstracting problems and the methodology in resolving problems

• Strong compiler backgrounds including MLIR/TVM/Triton/LLVM is desired

• Good knowledge of GPU architecture and fast kernel programming skills is a plus

• Knowledge of LLM algorithms or a certain HPC domain is a plus

• Knowledge of multi-GPU distributed communication is a plus

• Excellent oral communication in English is a plus

What you will be doing:

• Design and implementfunctional/performancetests for CUDA products, like driver and library.

• Automate CUDA tests, design test plans and integrate into automation testinginfrastructure.

• Triage test results, root cause test failures or performance drops, and drive through bugs to fix.

• Develop scripts/tools and optimize workflow to improve efficiency and productivity.

What we need to see:

• MS or PhD degree from a leading university in computer science or a related field.

• At least 3 years of relevant professional experience.

• Excellent QA sense, knowledge, and experience in software testing.

• Rich experience in test case development, tests automation and failure analysis.

• Proficient programming and debugging skills in C/C++ and Python.

• Comprehensive knowledge of Linux and Windows operating systems.

• Experience in using AI development tools for test plans creation, test cases development and test cases automation.

Ways to stand out from the crowd:

• Excellent English communication and collaboration skills.

• Strong understanding of CUDA, HPC, Gcov, VectorCAST, Coverity.

What you'll be doing:

• Work on NVIDIA's next generation of Video Decoder and Encoder hardware architecture.

• Research and study new video compression technology, specifications, papers etc.

• Develop c-model for algorithm study, hardware simulation and verification.

• Define the testplan, write architecture document, verify c-model and improve model coverage.

What we need to see:

• Master degree or above in Computer Science, Electronic Engineering.

• Minimum of 3 years' experience in the field of video technology ranging from codec, implementation, pre/post-processing, rate control and etc.

• Good programming skill and C/C++ coding abilities.

• Fluent English (both written and spoken) and good communication skill.

Ways to stand out from the crowd:

• Project experiences in video encoder, decoder or computer vision.

• Experience with video codec such as H264,HEVC, VP9, AV1or VVC.

• Experience with DL video/image processing.

• Creative, strong analysis, design and debug skill.

What you’ll be doing:

• Develop algorithms to exercise various parts of the GPU pipeline to verify our performance metrics.

• Deeply dive into NVIDIA GPU architecture and software stack, develop new feature for NVIDIA GPU performance profiling tools.

• Write unit and integration tests to verify the functionality, performance, stability, resource usage of our products.

What we need to see:

• Pursuing a Master's degree major in CS/SE.

• Proficiency in C/C++, object oriented programming.

• Proficiency in written and spoken English.

Ways to stand out from the crowd:

• OpenGL, GLES, Direct3D, Vulkan, CUDA, OpenCL, console graphics APIs.

• Experience of driver development.

• Background with software development for embedded systems.

What you’ll be doing:

• Lead, mentor, and grow a high-performing team of applied research engineers focused on humanoid loco-manipulation and mobile manipulation.

• Drive the definition, planning, and execution of projects involving foundation models (GR00T, Cosmos), Isaac Lab, and Newton workflows.

• Guide the team in advancing robot learning technologies and synthetic data generation from human video datasets.

• Hands-on design, implementation, and deployment of novel algorithms for humanoid robot locomotion and manipulation in simulation and real-world environments.

• Ensure seamless integration of applied research outputs with NVIDIA’s advanced robotics platforms.

• Foster a culture of innovation and collaboration, supporting deliverables such as prototypes, open source software contributions, patents, and publications in top conferences and journals.

• Collaborate cross-functionally with product, hardware, and software teams to translate research into impactful products.

• Support career development, performance management, and recruitment of top talent.

What we need to see:

• Advanced degree (PhD or Master’s) in Computer Science, Robotics, or a related field.

• 2 years of experience on technical leadership or team management in robotics, autonomous driving, machine learning, or related domains.

• Strong hands-on programming skills in Python and C++; experience with deep learning frameworks (PyTorch, JAX, TensorFlow) and physics simulation tools (Isaac Sim/Lab, MuJoCo).

• Excellent communication, organizational, and interpersonal skills.

• Experience with large-scale model training on GPU clusters.

• Hands-on experience on robotics simulation, sim-to-real and real-to-sim transfer.

• 5+ overall years of experience working on robotics technologies.

Ways to stand out from the crowd:

• Leadership in projects involving foundation models for robotics, includingVision-Language-Action(VLA) or Vision-Language Models (VLM).

• Experience with learning from human video demonstrations and human-object reconstruction.

• Expertise in humanoid loco-manipulation, encompassing whole-body control, dexterous and bimanual manipulation, and locomotion.

• Advanced knowledge in robot learning and reasoning, including imitation and reinforcement learning.

• Experience generating synthetic data for robotics applications.