Accelerated Compute Systems Performance 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.).

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 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 will be doing:

• Collaborate closely with our AI/ML researchers to make their ML models more efficient leading to significant productivity improvements and cost savings

• Build tools, frameworks, and apply ML techniques to detect & analyze efficiency bottlenecks and deliver productivity improvements for our researchers

• Work with researchers working on a variety of innovative ML workloads across Robotics, Autonomous vehicles, LLM’s, Videos and more

• Collaborate across the engineering organizations to deliver efficiency in our usage of hardware, software, and infrastructure

• Proactively monitor fleet wide utilization patterns, analyze existing inefficiency patterns, or discover new patterns, and deliver scalable solutions to solve them

• Keep up to date with the most recent developments in AI/ML technologies, frameworks, and successful strategies, and advocate for their integration within the organization.

What we need to see:

• BS or similar background in Computer Science or related area (or equivalent experience)

• Minimum 8+ years of experience designing and operating large scale compute infrastructure

• Strong understanding of modern ML techniques and tools

• Experience investigating, and resolving, training & inference performance end to end

• Debugging and optimization experience with NSight Systems and NSight Compute

• Experience with debugging large-scale distributed training using NCCL

• Proficiency in programming & scripting languages such as Python, Go, Bash, as well as familiarity with cloud computing platforms (e.g., AWS, GCP, Azure) in addition to experience with parallel computing frameworks and paradigms.

• Dedication to ongoing learning and staying updated on new technologies and innovative methods in the AI/ML infrastructure sector.

• Excellent communication and collaboration skills, with the ability to work effectively with teams and individuals of different backgrounds

Ways to stand out from the crowd:

• Background with NVIDIA GPUs, CUDA Programming, NCCL and MLPerf benchmarking

• Experience with Machine Learning and Deep Learning concepts, algorithms and models

• Familiarity with InfiniBand with IBOP and RDMA

• Understanding of fast, distributed storage systems like Lustre and GPFS for AI/HPC workloads

• Familiarity with deep learning frameworks like PyTorch and TensorFlow

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:

• Identify, run graphics, studio and WinAI benchmarks across servers, PCs, workstations and laptops.

• Compose competitive analysis reports for internal and external customers to position NVIDIA products appropriately using their evaluation.

• Develop and maintain automation scripts for games/studio/WinAI performance and system monitoring data collection on Windows and Linux to speed up providing business and engineering insights.

• Develop, implement and maintain tools to improve testing efficiency.

What we need to see:

• Pursuing BS in Computer Science or similar computer discipline.

• Good knowledge of Python or other scripting languages.

• Experienced and passionate about PC games or content creation.

• Linux and Windows knowledge.

• Good knowledge of PC systems and components.

• Capability to work with a lot of data.

• Good organizational, time management and task prioritization skills.

Ways to stand out from the crowd:

• Familiar with GenAI or LLM is a plus.

• Knowledge of OpenGL, Direct X and D3D.

• Knowledge of Data Visualization.

• Good knowledge of NVIDIA GeForce and RTX PRO series.

Intelligent machines powered by Artificial Intelligence computers that can learn, reason and interact with people are no longer science fiction. Today, a self-driving car powered by AI can meander through a country road at night and find its way. An AI-powered robot can learn motor skills through trial and error — this is truly an extraordinary time and the era of AI has begun.

• Develop, maintain and optimize performance KPIs necessary to deliver NVIDIA’s L2/L3/L4 autonomous driving solutions

• Devise acceleration strategies and patterns to improve software architecture and its efficiency on our computers with multiple heterogeneous hardware engines while meeting or exceeding product goals

• Develop highly efficient product code in C++, making use of algorithmic parallelism offered by GPGPU programming (CUDA)/ARM NEON while following quality and safety standards such as defined by MISRA

• Diagnose and fix performance issues reported on our target platform including on-road & simulation

• BS/MS or higher in computer science or a related engineering discipline

• Excellent C and C++ programming skills

• 10+ years of relevant industry experience

• Strong knowledge of programming and debugging techniques, especially for parallel architectures

• Good understanding of System SW / Operating Systems and Computer architecture

• Experience with performance analysis, optimizations and benchmarking

• You have excellent analytical, written and verbal interpersonal skills

• Understanding of Embedded architectures and Real-time operating systems & scheduling

• Strong mathematical fundamentals, including linear algebra and numerical methods

• Experience implementing algorithms in Robotics, Computer Vision and/or Machine Learning

• Software development experience with CUDA/GPGPU or any data parallel architectures