NVIDIA

Full-stack Engineer

• Lead the build and development of next-generation APIs, state management, and workflow orchestration systems that automate fleet lifecycle operations at a massive scale
• Drive technical alignment across dependent systems and partner teams to ensure cohesive integration, clear interfaces, and reliable end-to-end workflows, with a strong focus on delivery
• Act as a force-multiplier by coaching, mentoring, and encouraging senior engineers, elevating the technical standards and guidelines across the organization
• Maintain an incredible focus on the customer experience and product requirements, translating deep technical insight into high-impact business solutions
• Partner with executive and engineering leadership to codify critical business processes into self-measuring, scalable, and operationally consistent platforms, drastically reducing manual toil
• Direct the integration strategy for key technologies, including common AI schedulers (e.g., Kubernetes, Slurm) and innovative observability systems (e.g., Prometheus, OpenTelemetry, Grafana)

Principal Software Engineer – DGX Cloud

Backend Engineer

• Understand, modify, and improve an NVIDIA proprietary GPU compiler backend written in C++
• Design and develop new compiler passes and optimizations
• Work with global compiler, hardware and application teams
• Be part of a team that is at the center of deep-learning compiler technology

Backend Compiler Engineer

• Design, develop and deliver complex real-time applications for desktop and mobile platforms.
• Partner closely with operations for the reliability and scalability of our features.
• Build features and SDKs in a variety of languages, C++, TypeScript, Python, and Swift.
• React to user feedback and improve the product based on metrics collected from end-user and server systems.
• Collaborate and coordinate between multiple distributed teams for end-to-end feature delivery.

Senior System Software Engineer, GeForce NOW Client Platforms

Program Manager

• Full program ownership, with primary focus on: Comprehensive cross-functional schedules
• Ensure sound execution strategy is established and implemented
• Continuously assessing program risks and mitigation plans
• Review upstream POR (plan of record) changes and drive program strategy
• Own program POR, document strategy changes and rationale for decisions
• Drive understanding of Product Life Cycle, phase/achievement criteria, and cross-functional interdependencies
• Ensure cohesive collaboration between cross-functional teams and leads: architects, engineers, Hardware, Software, Firmware, Operations, Product Engineering, and Go-to-Market teams
• Lead project staff meetings to: Prioritize top issues, roadblocks, and areas of immediate focus
• Summarize concerns, blockades, and plans to resolve
• Report out program status to team members at all levels
• Implement AARs (after action review) from previous generation into current program
• Update program dashboards, rosters, and frameworks to scale communication across the organization

Product Program Manager

• Work on AI infrastructure networking and systems software for large-scale data-center networking stacks, focusing on performance, reliability, and scalability.
• Develop and optimize software for ConnectX SmartNICs and BlueField DPUs, including drivers, firmware, and DOCA/DPDK-based data-plane applications.
• Work on RDMA (RoCE/Ethernet) networking to improve low-latency, high-throughput communication between GPUs, NICs, and DPUs in distributed AI/HPC systems.
• Debug and tune end-to-end networking performance in large-scale distributed training environments.

Networking Systems Software Engineering Intern

Engineer

• Provide stewardship for PTX ISA and PTX Compiler infrastructure for Generative AI and DL.
• Collaborating with architecture and programming model teams to design and implement programming models for next generation GPUs.
• Working closely with others to help design compilation stack and strategies for AI and DL workloads.
• Collaborate closely with teams developing other related components to ensure compatibility, robustness and high-quality code generation.

Senior GPU Compiler Development Engineer

• Lead the architecture and delivery of high-scale web products across frontend, backend services, and data layers, with clear availability and latency targets (SLOs/SLAs).
• Own multi-team initiatives end to end: problem discovery, RFCs/design reviews, phased rollouts, and success metrics tied to product and business outcomes.
• Drive reliability, performance, and observability improvements to meet exascale standards.
• Establish engineering standards and reusable platforms/design systems to reduce complexity, support load and long-term tech debt.
• Collaborate with NVIDIA AI Research teams to identify pain points and deliver the next generation user experience that accelerates their work.
• Mentor and sponsor engineers; improve code quality, testing, security, and observability through reviews, pairing, and coaching.
• Stay ahead of AI/ML infrastructure trends and drive adoption of best practices within the team.

Senior Full-Stack Lead Engineer

Robotics

• Build highly scalable, robust, and efficient CI/CD frameworks.
• The workload is data intensive and requires CPU/GPU heterogeneous computation.
• Build world-class visualization tools for analyzing and optimizing for all our datasets and compute jobs (across 10s of thousands of GPUs)
• Develop and apply AI agents to significantly improve programming efficiency within the team, and decrease the human effort in fixing job failures
• Overall, collaborate with researchers to gather requirements, understand tooling / visualization / automation needs, and deliver full-stack solutions that move the needle with speed of light.

Senior Software Engineer – Robotics Research

• Create new technical designs and implement features while working in an agile environment.
• Improve existing tools for NVIDIA GPUs, enabling developers to quickly identify and resolve rendering and performance issues.
• Create innovative new tools that bridge AI & graphics in next generation workflows.
• Collaborate closely with internal and external partners, including teams within NVIDIA such as the Compiler, Driver, Research, and GPU Architecture teams.
• Drive technology discussions and provide feedback on system architecture.
• Accurately estimate and prioritize tasks in order to create realistic delivery schedules.
• Write fast, effective, maintainable, reliable and well-documented object-oriented code.
• Document requirements and designs, and review documents with key partners.
• Meet with the QA Department to develop test plans for new features.

Software Engineer, Neural Graphics Developer Tools

Solutions Engineer

• Partner with AI-native / consumer internet customers on large data center GPU and networking deployments.
• Guide architecture decisions across network, compute, and storage, including fabric design.
• Support on-site bring-up of server, network, and cluster infrastructure in customer data centers.
• Demonstrate expertise on advanced GPU and network systems (Spectrum-X, BlueField DPU, InfiniBand/RoCE, etc.) for key accounts.
• Run regular technical account reviews covering roadmap alignment, cluster issues, feature discussions, and new technology introductions.
• Capture customer-specific requirements and translate them into concrete feedback for product, architecture, and engineering teams.
• Analyze and debug configuration and performance issues in RoCE and InfiniBand environments.
• Work across NICs, switches, Linux, and system software to deliver performant, reliable AI clusters.
• Identify and shape new project opportunities for NVIDIA GPUs, networking, and software in AI and data center use cases.
• Collaborate closely with Systems Engineering, Product Management, and Sales to align solutions with customer outcomes.
• Build targeted POCs that showcase the value of NVIDIA’s networking stack (e.g., Spectrum-X fabrics, BlueField DPUs) in real customer environments.

Senior Network Solution Architect – AI Fabrics

Machine Learning Engineer

• Engage closely with our AI and ML research teams to discern their infrastructure requirements and barriers
• Convert those insights into actionable improvements
• Proactively identify researcher efficiency bottlenecks and lead initiatives to systematically improve it
• Drive the direction and long-term roadmaps for such initiatives
• Monitor and optimize the performance of our infrastructure ensuring high availability, scalability, and efficient resource utilization
• Help define and improve important measures of AI researcher efficiency
• Work closely with a variety of teams, such as researchers, data engineers, and DevOps professionals
• Keep up to date with the most recent developments in AI/ML technologies, frameworks, and successful strategies

Principal AI and ML Infra Software Engineer, GPU Clusters

• Build flows and methodology around vendor EDA tools to streamline circuit design and verification workflows.
• Bring up compiler workspaces for new projects and ensure all internal flows, infrastructure, and tools are in sync across a multi-project development environment.
• Develop and maintain regression suites to validate design quality before releases.
• Own and manage the release process for custom RAM circuit macros delivered to Place-and-Route (PNR) teams.
• Ensure seamless integration of RAM compilers into PNR flows across NVIDIA.
• Maintain versioned records of tool and infrastructure for each design release.
• Own the web front-end that circuit designers use to access simulation data and interact with design collateral.
• Collaborate with circuit design, layout, technology, and 3rd party EDA vendor teams distributed across geographies and time zones.

Senior VLSI CAD Engineer

DevOps Engineer

• Working closely with compiler development and verification engineers on improving automated build pipelines and processes
• Developing DevOps tools and workflows for CI/CD
• Crafting efficiency and usability improvements across NVIDIA proprietary products
• Supporting thousands of developers, focusing on predictability, automation, and reliability
• Proposing enhancements based on safety, compliance, and other requirements

Build and DevOps Engineer – Compilers

Architect

• Research, architect, implement, and evaluate mechanisms for capturing and studying complex applications suitable for architectural and microarchitectural CPU analysis in simulation.
• Implement tools, processes, and systems for collecting traces and checkpoints for complex multi-threaded heterogeneous applications and support other architects in using those tools to study workloads.
• Contribute to developing functional and performance models of ARM-based systems.
• Focus on infrastructure for recording and replaying workload sequences for performance and power analysis.
• Stay on top of guidelines in industry and academia relating to simulation, checkpointing, tracing, deterministic replay, and architectural/microarchitectural analysis of complex heterogeneous computer systems.

Senior CPU Workloads and Simulation Architect

Administration

• Serve as the CLM systems administrator, managing platform configuration, user provisioning, roles and permissions, workflows, templates, clause libraries, and playbooks to meet evolving business needs.
• Build and maintain electronic intake forms (eForms) and approval workflows to ensure contract requests are routed to the right collaborators with accurate data.
• Partner with business users to translate needs into technical requirements for system build, ongoing improvements, and iterative refinement.
• Develop and maintain integrations between the CLM platform and adjacent systems such as CRM, ERP, procurement, e-signature, and document management using REST APIs, middleware, webhooks, and native connectors.
• Maintain governance of solutions, drive standard methodologies across business divisions, and serve as a liaison between business and IT.
• Support implementation cycles, UAT testing, and platform upgrades, and guide master data and taxonomy for migrations.
• Provide dashboards, reports, and insights for critical metrics, contract status, compliance, and risk management.
• Train power users, develop enablement materials, and promote user adoption and effective usage across the organization.
• Evaluate and implement AI-powered platform features to streamline operations and reduce manual effort.