英伟达Senior Software Engineer, Enterprise AI Software
任职要求
• A history of using advanced programming skills to build distributed compute systems, backend services, microservices, and cloud technologies. • Experience productionizing and deploying LLM models. • Effective experience working with multi-functional teams, principals, and architects across organizational boundaries. • Mentorship and the ability to grow teams and team members. • Deep technical expertise in distributed containerized applications using Docker, Kubernetes, Helm Charts. • Passion for building scalable and performant microservice applications. • Excellent interpersonal skills and the flexibility to lead multi-functional efforts. • Proven experience debugging and analyzing the performance of distributed microservices or cloud systems. • A degree in Computer Science, Computer Engineering, or a related field (BS or MS) or equivalent experience. • 6+ years of demonstrated experience in developing performant microservices, cloud software, and/or tooling roles. Ways to stand out from the crowd: • Experience with open-source inference engines and serving stacks. • Experience benchmarking the speed and accuracy of generative AI models. • Prior experience in building and deploying containers for microservices, cloud, and on-prem deployments, along with their associated CI/CD pipelines. • Previous work in large-scale backend development. We are widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and creative people in the world working for us. If you're creative and autonomous with a real passion for technology, we want to hear from you.
工作职责
• Design, build, and optimize containerized inference execution for LLM applications, ensuring efficiency and scalability. These applications may run in container orchestration platforms like Kubernetes to enable scalable and robust deployment. • Ensure the performance and scalability of NIMs through comprehensive performance measurement and optimization. • Apply container expertise to create and optimize the basic building blocks of NIMs, influencing the development of many models and related products within NVIDIA. • Collaborate, brainstorm, and improve the designs of inference solutions and APIs with a broad team of software engineers, researchers, SREs, and product management. • Mentor and collaborate with team members and other teams to foster growth and development. Demonstrate a history of learning and enhancing both personal skills and those of colleagues.
• Design, build, and harden containers for NIM runtimes, inference backends; enable reproducible, multi-arch, CUDA-optimized builds. • Develop Python tooling and services for build orchestration, CI/CD integrations, Helm/Operator automation, and test harnesses; enforce quality with typing, linting, and unit/integration tests. • Help design and evolve Kubernetes deployment patterns for NIMs, including GPU scheduling, autoscaling, and multi-cluster rollouts. • Optimize container performance: layer layout, startup time, build caching, runtime memory/IO, network, and GPU utilization; instrument with metrics and tracing. • Evolve the base image strategy, dependency management, and artifact/registry topology. • Collaborate across research, backend, SRE, and product teams to ensure day-0 availability of new models. • Mentor teammates; set high engineering standards for container quality, security, and operability.
NVIDIA data center systems, such as DGX and HGX, have become core to NVIDIA's rapidly growing enterprise and cloud provider businesses. These platforms bring together the full power of NVIDIA GPUs, NVIDIA NVLink, NVIDIA InfiniBand networking, NVIDIA Grace CPUs, and a fully optimized NVIDIA AI and HPC software stack. We are hiring Sr. Software Engineer who will help build simulators for our DGX Server platforms. Simulations play a significant role in building scalable systems at Speed of Light! You will work with world class engineering teams across HW and SW. What you’ll be doing: • Contribute to architect and develop simulation platform for next-gen NVIDIA DGX platforms. • Build, integrate and enhance simulator components with new HW features and write supporting technical documents. • Bring full SW stack up on DGX Simulator; work closely with hardware modeling, kernel & platform driver teams distributed globally. • Improve performance, fix bugs across user and kernel stack, and automate execution flow.
• Design, develop, and improve scalable infrastructure to support the next generation of AI applications, including copilots and agentic tools. • Drive improvements in architecture, performance, and reliability, enabling teams to bring to bear LLMs and advanced agent frameworks at scale. • Collaborate across hardware, software, and research teams, mentoring and supporting peers while encouraging best engineering practices and a culture of technical excellence. • Stay informed of the latest advancements in AI infrastructure and contribute to continuous innovation across the organization.
Today, NVIDIA is tapping into the unlimited potential of AI to define the next era of computing! An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, encouraging environment where everyone is inspired to do their best work. Come join the team and see how we can make a lasting impact on the world.NVIDIA is hiring senior software engineers in its Infrastructure, Planning and Process Team (IPP), to accelerate AI adoption across various engineering workflows within the company. IPP is a global organization within NVIDIA. The group works with various other teams within NVIDIA such as Graphics Processors, Mobile Processors, Deep Learning, Artificial Intelligence and Driverless Cars to cater to their infrastructure and software development workflow needs. As a senior engineer on AI Workflow, you will create and establish tools and software solutions that leverage Large Language Models and agentic AI to automate end to end software engineering workflows and enhance the productivity of engineers across NVIDIA. What you’ll be doing: • Develop and implement solutions throughout software development lifecycles to improve developer efficiency, accelerate feedback loops, and boost release reliability • Experience designing, developing, and deploying AI agents to automate software development workflows and processes. • Continuously measure and report on the impact of AI interventions, showing progress in metrics such as cycle time, change failure rate, and mean time to recovery (MTTR). • Build and deploy predictive models to identify high-risk commits, forecast potential build failures, and flag changes that have a high probability of failures. • Research emerging AI technologies and engineering best practices to continuously evolve our development ecosystem and maintain a competitive edge.