Machine Learning Software Platform Architect jobs at Nvidia in China, Shanghai

What you’ll be doing:

• Develop and test deep learning models for object detection, tracking, and behavior prediction.

• Preprocess and analyze sensor data from LiDAR, radar, and camera systems.

• Collaborate with cross-functional teams on algorithm integration and performance evaluation.

• Support data labeling, simulation, and validation pipelines.

• Document model performance and contribute to internal reports.

What we need to see:

• Enrolled in a Bachelor’s, Master’s, or PhD program in Computer Science, Electrical Engineering, Robotics, or a related field.

• Strong knowledge of Python and libraries such as PyTorch or TensorFlow.

• Familiarity with computer vision, deep learning, and reinforcement learning concepts.

• Experience with autonomous systems, robotics, or simulation tools a plus.

• Internship duration: 10–12 weeks (with potential for extension)

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.).

You will be part of global Performance Lab team, improving our capacity to expertly and accurately benchmark state-of-the-art datacenter applications and products. We also work to develop infrastructures and solutions that enhance the team’s ability to gather data through automation and designing efficient processes for testing a wide variety of applications and hardware. The data that we collect drives marketing/sales collaterals as well as engineering studies for future products. You will have the opportunity to work with multi-functional teams and in a dynamic environment where multiple projects will be active at once and priorities may shift frequently.

What you’ll be doing:

• Benchmark, profile, and analyze the performance of AI workloads specifically tailored for large-scale LLM training and inference, as well as High-Performance Computing (HPC) on NVIDIA supercomputers and distributed systems.

• Aggregate and produce written reports with the testing data for internal sales, marketing, SW, and HW teams.

• Develop Python scripts to automate the testing of various applications.

• Collaborate with internal teams to debug and improve performance issues.

• Assist with the development of tools and processes that improve our ability to perform automated testing.

• Setup and configure systems with appropriate hardware and software to run benchmarks.

What we need to see:

• Currently pursuing a bachelor's degree (or higher) in Computer Science, Electrical Engineering, or a related field.

• Experienced in programming and debugging with scripting languages such as Python or Unix shell.

• Strong data analysis skills and the ability to summarize findings in a written report.

• Hands-on experience with Linux based systems. Familiarity using a container platform such as Docker or Singularity. Experience with compiling and running software from source code.

• Good English verbal and written skills to improve collaboration with coworkers.

• Fast and self-learning capabilities.

Ways to stand out from the crowd:

• Experience with CI/CD pipelines and modern DevOps practices. Familiar with cloud provisioning and scheduling tools (Kubernetes, SLURM).

• Curiosity about GPUs, TPUs, cloud and performance benchmarking.

• Familiar with ML/DL techniques, algorithms and frameworks like TensorFlow or PyTorch. Experience in AI model inference deployment and training launching.

• Background of system-level problem solving.

What you'll be doing:

As a valued member of the team, you will be involved in the technical design and implementation of numerous features working in an agile environment. In this role you can expect to:

• Create developer tools features for NVIDIA GPUs that enables developers to quickly iterate on optimizations to build fast graphics applications.

• Write fast, effective, maintainable, reliable and well documented code.

• Effectively estimate and prioritize tasks in order to build a realistic delivery schedule.

• Provide peer reviews to other engineers including feedback on performance, scalability and correctness.

• Drive technology discussions and provide valuable feedback about the architecture as a domain expert.

• Document requirements and designs, and review documents with stakeholders.

• Demonstrate growth in technical and non-technical abilities.

• Meet with the QA Department to develop a test plan for new features.

What we need to see:

• Pursuing BS or MS degree in one of the areas of Electrical Engineering, Computer Engineering, Computer Science.

• Excellent C++ programming skills and ability to articulate key aspects of Object-Oriented Programming.

• Proficient in at least one graphics programming API such as Direct3D, OpenGL and Vulkan.

• Able to work effectively with a team of engineers in a fast paced and dynamic environment.

• Excellent written and verbal communication skills.

• Able to estimate effectively to ensure delivery of software on time.

Ways to stand out from the crowd:

• Knowledge of 3D Graphics Algorithms and GPU Architectures.

• Strong grasp of heterogeneous computing, multithreading and a deep understanding of streaming multiprocessors, warp scheduling etc...

• Experience with GPU low-level performance tuning/optimization, including profiling and debugging.

• Solid understanding of User Experience (UX) design, GUI development and the Qt framework is a huge plus.

What you’ll be doing:

• Design and Implement API tests for CUDA driver and library.

• Automate CUDA tests, design test plan and enable them in automation testing infrastructure.

• Triage test results, isolate test failures and improve test coverage.

What we need to see:

• Can work 4 days a week for at least 1 year

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

• Familiar with programming and debugging skills with C/C++ and Python.

• Interested in test cases development, tests automation and failure analysis.

• Experience using AI development tools to improve quality and productivity across the end-to-end QA workflow.

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

Ways to stand out from the crowd:

• Strong English communication and collaboration skills.

• Familiar with parallel programming, ideally CUDA C/C++, is a plus.

• Background with Bullyseye, Gcov or other dev tool is a plus.

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.