Senior Compiler Verification Engineer - Deep Learning 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

• Develop comprehensive test plans and implement them using formal verification tools.

• Create assertions, assumptions, and cover properties to validate design behavior.

• Employ abstraction techniques to manage complexity and achieve verification goals.

• Analyze and debug failures in the RTL design.

• Collaborate with architecture, design, and verification teams to communicate coverage and ensure design quality.

What we need to see:

• BS/MS/PhD in CS, CE, EE, or Mathematics (or equivalent experience)with 3+ years relevant experience

• Strong problem-solving skills and the ability to thrive in a collaborative, distributed team environment.

• Proficiency in scripting languages like TCL, Python, or Perl.

Ways to stand out from the crowd:

• Hands-on experience with formal verification tools such as JasperGold or VC Formal.

• Experience with formal verification methodologies, SystemVerilog, and temporal logic.

What you'll be doing:

• Working with customers, partners, and IP vendors to understand SOC/IP solutions best suited for the target use cases and work with them to select and integrate appropriate IP/SOC solutions.

• Work with Architects, Chip Leads, and Customers on SOC/IP design, development, timing closure, power analysis, methodology alignment, and program execution to ensure pre-silicon and post-silicon targets are met.

• Integrating, evolving, and optimizing IP blocks across a range of products and use cases for

  NVIDIA
  SoCs in AI, driving, 6G, cloud, gaming, and other applications.

• Working with teams throughout the company (Architects, RTL, PD, Circuit, SI, Thermal, SW, Platform, Operations, Marketing, etc...) on implementing cross-team solutions to achieve project targets.

• Drive cross-team methodologies for external soft IP and PHY integration, Nvidia IP release to partner, RTL development and microarchitecture.

What we need to see:

• B.S. or M.S. in Computer Engineering or Electrical Engineering (or equivalent experience)

• 9+ years of relevant work experience in RTL development focused on CPU

  , GPU,
  and high-performance architectures.

• Proficiency in industry-standard RTL development and synthesis tools.

• Experience developing high-speed digital blocks.

• Experience debugging complex microarchitectural structures

• Strong interpersonal, communication, and teamwork skills.

• A drive to continuously learn and expand architectural breadth and depth.

• Ability to evaluate microarchitectural options for tradeoffs across design, verification, and PD.

• Experience interconnecting and analyzing complex microarchitectural structures and subsystems.

Ways to stand out from the crowd:

• Cross-cultural work and study experience

• Experience in ARM-based SOC definition and development

• Background in partner and customer engagement for usecase/chip solution.

• Experience in some of domains, SOC clock implementation, power structure insertion, DFT, synthesis, place and route and STA.

What You Will Be Doing:

• Understand, modify, and improve an NVIDIA proprietary GPU compiler and assembler written in C++

• Design and develop new compiler passes and optimizations to produce best-in-class, robust, supportable compiler and tools

• Work with global compiler, hardware and application teams to oversee improvements and problem resolutions

• Be part of a team that is at the center of deep-learning compiler technology spanning architecture design and support through functional languages

What We Need To See:

• Pursuing B.S. or higher degree in Computer Science/Engineering

• Excellent hands-on C++ programming skills

• Strong background in software engineering principles with a focus on crafting robust and maintainable solutions to challenging problems

• Good communication and documentation skills and self-motivated

Ways To Stand Out From The Crowd:

• Masters or PhD preferred, with some compiler code generation experience

• Experience in developing CUDA, DirectX, OpenGL/Vulkan applications

• Experience in performance analysis and compile time analysis tools

• Exposure to various ML techniques

What you’ll be doing:

• A role in physical design for NVIDIA GPU and Mobile chips.

• Participate in various aspects of physical design, including full chip floorplanning, power/clock distribution, timing optimization, place & route, timing closure, power/signal integrity analysis, and physical verification.

• Troubleshoot a wide variety of design and flow complicated issues, and apply proactive intervention Collaborate with RTL, DFT and Circuit designers to ensure high quality of design implementation.

What we need to see:

• BS in Engineering or Science or equivalent experience.

• Power user of EDA tools from Synopsys(ICC2/FC/PT/STAR-RC),Cadence(EDI/Innovus/Voltus).

• Experience in Clock/Power Distribution, P&R, Timing closure, RC Extraction, and verification on advanced technology nodes.

• 2+ years of experience in above areas.

Ways to stand out from the crowd:

• MS in Engineering or Science.

• Knowledge in FinFET technology, circuit design, and package design.

• Experience in physical verification tools from Synopsys (ICV) or Mentor (Calibre).

• Proficiency in Perl, Python, TCL and Makefile scripts.