דרושים Deep Learning Performance Architect Intern ב-אנבידיה ב-China, Shanghai

What you’ll be doing:

• Utilizing AI-powered tools to enhance QA efficiency, including optimizing test coverage, identifying high-risk areas in software systems, automating test case generation, defect detection, and regression testing.

• Work closely with multi-functional teams to understand the test requirements and take ownership of product quality.

• Develop test plans, design test cases, complete testing via automation and/or manually and compose test reports.

• Build and maintain our complicated test environments.

• Manage bug lifecycle and co-work with inter-groups to drive for solutions.

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

• Report bugs found during execution, assist with reproduction and debugs to understand root cause, verify bug fixes provided by R&D team, raise if not fixed.

• Experience in using AI development tools. Adept at creating detailed test cases, automating them, increasing code coverage, identifying valid bugs early on, and solving these bugs swiftly.

What we need to see:

• Currently pursuing a Master degree inTelecommunication/ComputerScience/Electronic Engineering/Computer Engineering or equivalent.

• Background with LTE/5G MAC and PHY from both systems and low-level 3GPP spec point of view.

• Interested in understanding QA methodologies.

• Familiarity with AI-powered testing frameworks and platforms that improve process efficiency

• Proficient Linux experience and shell/python/perl programming skills.

• Problem solving skills.

• We love people who are highly motivated, have excellent interpersonal skills, and can demonstrate good work ethics and a high sense of teamwork

Ways to stand out from the crowd:

• Proficient experience in Keysight equipment.

• Background with working with NVIDIA GPU and/or DPU hardware is a strong plus.

• Experience in using or managing cloud technologies like Kubernetes, OpenStack and Docker.

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 VectorCAST, Gcov or other dev tool is a plus.

What you’ll be doing:

• Be responsible for functionality, compatibility, and performance tests in NVIDIA AI software stack release.

• Develop, maintain, and improve test automation infrastructure with using AI tools.

• Work with development teams to triage issues, root cause analysis, verify fixes, define new tests, improve test plans.

What we need to see:

• Pursuing MS or higher degree in CS/EE/CE.

• Proven success in leveraging AI tools to significantly improve efficiency, streamline workflows or enhance process automation.

• Good python, C++ programming skillset, Linux knowledge is required.

• Experience in software development withpopular AI modelsis a strong plus.

• Good communication skills, fluent oral and written English.

Ways to stand out from the crowd:

• Be familiar with deep neural network training, inference, optimization in typical DL Frameworks.

• Background with NVIDIA GPU Computing (CUDA) is a strong plus.

What you’ll be doing:

In this role you will work closely with deep learning compiler developers to verify new and state of the art deep learning related features and components including implementing and executing functional and performance testing and benchmarking software solutions. This would include implementing verification programs, tools, scripts, and libraries. You will apply deep learning and other sophisticated techniques to implement compiler verification solutions.

What we need to see:

• Pursuing BS/MS/PhD in Computer Science, Computer/Electrical Engineering, Mathematics or equivalent program.

• Strong Python programming skills

• Strong modern C++ programming skills

• Good understanding of machine learning domain and concepts, including Large Language Models (LLM)

Ways to stand out from the crowd:

• Knowledge of deep learning frameworks such as Pytorch, Scikit Learn, JAX/XLA or TensorRT

• Background of other programming languages and domains such as CUDA, Docker and GPU-Accelerated Cloud

What you’ll be doing:

• Be responsible for executing test cases to validate NVIDIA GPU Cloud products

• Automate test cases and maintain the automation scripts

• Work with development teams to triage issues, root cause analysis, verify fixes, define new tests, improve test plans

What we need to see:

• CurrentlypursuingMaster's degrees in Computer Science and Electronic Engineering

• Fluent oral and written English

• Experience in using AI development tools to improve quality and productivity across the end-to-end QA workflow, including test plan creation, test case development, and test automation​

• Proficient in Python and shell scripting

• Good Knowledge on Linux OS

• QA sense, knowledge, and experience in software testing

• Great problem-solving skills

• Good interpersonal skills, quick learner, proactive, innovative, and committed

• Strongly self-motivated with a passion to learn new hardcore technology

• Able toworkfull time up to 1 year

Ways to stand out from the crowd:

• Experience working with NVIDIA GPU hardware is a strong plus

• Background in parallel programming ideally is a plus

• Experience with virtualization technologies like Docker, Kubernetes, Openstack

• Get familiar with various GPU workload’s composition

• Learn about what’s the usual feature metrics for GPU workload

• Design and implement inventive solution to efficiently extract features from GPU workload

• Verify the solution using direct and random GPU workload

• Design and implement inventive solution simplify GPU workload while keeping the required features

• Design and implement inventive solution to generate GPU workload according to required features

• Design and implement inventive solution to generate GPU workload which has the same feature with a given test and randomize other (required) features

• Thoroughly verify the solution on GPU functional simulator/full chipRTL/emulation/siliconplatform.

• Provide detailed and organized documentation and report out for the project.

• Good communication and problem analysis ability

• Shown knowledge of DL algorithms

• Experience of training and fine-tuning model

• Experience of building and improving own model

• Bachelor in CS or EE. MS, PhD or equivalent is a plus.

• Knowledge of GPU architecture

• Experience of building AI agent