דרושים Deep Learning Performance Architect ב-אנבידיה ב-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.

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'll be doing:

• Developing and introducing groundbreaking reinforcement learning algorithms tailored for LLM applications.

• Collaborating with a world-class team of engineers and researchers to integrate these algorithms into applied scenarios.

• Using your extensive expertise in math and AI to improve the reasoning capabilities of our models.

• Engaging in rigorous testing and refinement processes to ensure flawless performance and reliability.

• Contributing to our collective goal of delivering industry-leading AI solutions, strictly adhering to NVIDIA's high standards.

What we need to see:

• Proficient in C++/Python programming.

• 3+ years working experience.

• BS or MS (or equivalent experience) in CS, CE, EE, or a related field.

• Proven experience in reinforcement learning and its application to large language models.

• Strong background in mathematics and AI algorithms, with a focus on reinforcement learning.

• Demonstrated history of applying reinforcement learning algorithms in practical scenarios.

• Understanding of GPU architecture is a huge plus.

• Excellent problem-solving skills and the ability to work collaboratively in a dynamic team environment.

• A passion for innovation and a dedication to achieving outstanding results.

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:
• Work on research, design and implementation of software features that are beneficial to our customers to meet their performance targets and build unique values

• World model and nvidia cosmos related development and bug fixes

• Develop solutions for DNN models acceleration optimization and deployment.

What we need to see:
• Subject to arrangement to different works at any time to take on different tasks and challenges

• Self-motivated attitude and motivation to make things success, intent to learn

• BS/MS degree in Computer Science/EE or related

• Proven fundamentals in c++/python programming and SW design and debug skills

• Abundant knowledge of ML/DL techniques for Computer Vision and autonomous driving

• Believe that experimenting is the only way to find the truth, not argument and arbitary guess

• Located in Shanghai or willing to work in Shanghai

Ways to stand out from the crowd:
• DNN development and network acceleration is highly desired

• Familiarity with GPU computing/NVIDIA CUDA/NVIDIA TensorRT