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Build efficient, low-latency software for AI media workloads across cloud and embedded platforms. Optimize and deploy AI models and software stacks on embedded processors and NVIDIA GPU platforms. Profile AI workloads to improve performance, power, and memory usage.
Build and deploy: Design and implement network automation for new builds and bring-ups, from zero-touch provisioning and config generation to validation and handoff to operations.
NVIDIA is looking for a Senior Technical Program Manager to partner with Production Engineering and drive execution across critical DGX Cloud initiatives.
Lead design and development of ground breaking end-to-end reference system stack for 5G/6G baseband system. Optimizing CPU, GPU and NIC sub-systems for predictable low-latency and maximum throughput.
Own technical product marketing and developer go-to-market for NVIDIA Cosmos Framework!
Serve as a trusted advisor who understands complex or large-scale customer workloads and maps them to NVIDIA’s technology stack to achieve measurable outcomes.
Developing comprehensive FV testplan documents. Identifying key behaviors for verification of DUT and creating a verification plan. Developing verification environment including environment assumptions, assertions and cover properties in context of the verification plan.
Drive complex technical programs focused on infrastructure operations, service health, operational readiness, and platform reliability. Partner with engineering, operations, and reliability teams to improve infrastructure visibility and accelerate issue resolution.
Work together with infrastructure and platform leaders to drive forward strategic technical programs across cloud infrastructure and platform software.
As a Senior Staff Software Engineer, you will define how NVIDIA builds and operates autonomous enterprise systems. Your influence will be measured in systems adopted, engineers elevated, and problems that stopped being problems.
Leading both the DSX Software and DSX Controls and Monitoring groups, you will recruit and develop engineers, technical leads, and managers.
Develop and construct the store, data model, and query path for engineering performance data. This includes ingesting, joining, storing, and delivering it at the scale of a GPU data center with low latency.
Analyze the performance of distributed training and inference workloads throughout the entire stack: model and framework, data loading and checkpointing, compiler and kernels, CUDA and NCCL, host and network.
Define and maintain the performance data requirements from various sources including profiling libraries, annotation APIs, frameworks, runtimes, communication libraries, schedulers, configurations, and infrastructure telemetry.
Develop and tune synthetic GPU workloads using CUDA or GPU programming DSLs, and application workload recipes using AI training and inference frameworks, to stress targeted hardware subsystems and expand validation coverage.
Define the end-to-end architecture: the data model, the service and API contracts, and the boundaries with the tools and teams that produce the data. Make foundational decisions and justify them in documents. Include considered alternatives and evidence that could reverse them.
Develop and maintain data models for pricing and rebate analysis, consolidating and curating data from multiple systems into a single source of truth. Collaborate with multi-functional teams to find opportunities for process optimization, automation, and standardization.
Research, design, develop and implement architecture solutions meeting internal and external security requirements and standards. Apply innovative security primitives to enable next generation secure platforms.
Own the product vision, strategy, and roadmap for training data acquisition and preparation. Translate research priorities and model capability gaps into source requirements, platform investments, and dataset acceptance criteria aligned with training schedules.
Developing physical design methodologies for implementation of graphics processors and SOCs. Key responsibility includes developing unique and creative solutions to the state of the art physical design problems that are needed for NVIDIA chips.
Own the customer relationship end to end. Build trusted executive partnerships across engineering, information technology, manufacturing, and corporate strategy.
Shape the Future of Validation. Design and build the systems that turn cluster validation from a manual exercise into repeatable, verifiable software. Your work will directly shape how NVIDIA and its partners qualify new GPU platforms and deployments.
As an Account Manager, you’ll be a key partner to our digital health clients, helping them use NVIDIA’s AI platform to solve their most complex problems and achieve their goals.
You are going to work directly with end customers, NVIDIA channel partners, and the extended sales team to achieve and exceed growth targets across a set of named manufacturing accounts in the United States.
US, USA