Jobs at NVIDIA

Lead major incident response and set technical direction for site reliability engineering across NVIDIA’s enterprise platforms. Design and operate distributed, Kubernetes-based cloud infrastructure; build automation, observability, self-healing systems, and AI-assisted incident tooling. Drive root cause analysis, SLOs, error budgets, and systemic reliability improvements. Partner with Cloud, Platform, Security, and AI/ML teams, mentor engineers, influence architecture, and communicate with executives during critical incidents.

Support enterprise customers deploying NVIDIA AI Enterprise across cloud and datacenter environments. Troubleshoot complex software issues, reproduce failures, collect diagnostics, and partner with engineering on fixes. Build Python automation, diagnostics, test harnesses, Kubernetes deployment assets, patches, documentation, and runbooks. Work with Linux, containers, GPUs, AI frameworks, inference services, distributed systems, and cloud platforms. Own customer escalations through resolution and participate in a monthly weekend Sev1 on-call rotation.

Lead NVIDIA’s graphics systems team in developing and productionizing an agentic software engineering framework. Responsibilities include defining strategy and transformation roadmaps, applying agents across requirements, implementation, validation, review, and release, maintaining graphics and display product commitments, growing engineering talent, and packaging the resulting orchestration, tools, evaluations, and operating practices for adoption across NVIDIA.

Develop and automate tests for NVIDIA’s embedded, GPU-based, and robotics software and hardware stack. Responsibilities include building test strategies, improving code coverage, debugging regressions, creating CI/CD workflows, developing AI-driven testing agents, evaluating model performance, and reporting quality metrics. The role collaborates with hardware, software, release, and project teams while supporting robotics simulation, perception, navigation, manipulation, and functional safety validation.

Lead operational readiness for NVIDIA Cloud Partners running large-scale GPU infrastructure. Build continuous validation, observability, automated detection and remediation, fleet lifecycle management, configuration management, and operational frameworks across Linux, Kubernetes, networking, storage, and AI workloads. Define health signals, SLOs, readiness criteria, runbooks, and reusable automation while collaborating directly with partner engineering and operations teams.

Develop and deploy large-scale AI solutions, distributed training and inference systems, and MLOps pipelines on cloud and NVIDIA platforms. Advise customers, diagnose full-stack machine learning issues, optimize performance, latency, cost, and reliability, support new hardware in open-source frameworks, and create open-source tools and reference architectures. Collaborate with infrastructure and accelerated-framework teams while communicating technical architectures and recommendations to engineering and leadership audiences.

Design, build, and optimize high-performance full-stack applications for web, desktop, mobile, and TV platforms. Develop responsive native and web interfaces using Angular, React, JavaScript, and related technologies; consume RESTful APIs; improve reliability, scalability, security, and cross-platform performance; create CI scripts; and collaborate with product, design, operations, and architecture teams.

Build and optimize high-performance local AI inference software for NVIDIA RTX and DGX GPUs. Develop inference runtimes and execution stacks, optimize models and data pipelines using quantization, pruning, sparsity, and distillation, and perform system-level debugging and performance analysis. Collaborate with software, research, architecture, and product teams to improve scalability, stability, accuracy, and production readiness across language, vision, speech, and diffusion workloads.

Design, develop, and deploy AI-powered automation for hardware performance verification. Build intelligent tools and agents for test generation, regression detection, failure analysis, and corrective recommendations. Integrate LLM assistants, create verification data pipelines, establish impact dashboards, and collaborate with verification engineers. Apply generative AI, reinforcement learning, and formal methods to improve coverage, reduce cycle times, and increase productivity across complex GPU, CPU, memory, and HPC verification workflows.

Architect, build, and maintain enterprise AWS infrastructure using Terraform; develop and refine Jenkins CI/CD pipelines for zero-downtime releases; operate and optimize EKS/ECS, Lambda, and other services; implement proactive AI-driven observability; define SLOs/SLAs, lead incident response, and improve platform resilience and cost-efficiency.

Lead the architecture and development of scalable full-stack Sales AI applications. Integrate generative AI models, retrieval systems, APIs, and agentic workflows into secure production solutions. Guide architecture across front-end, back-end, data, integrations, and cloud infrastructure while improving testing, delivery, observability, reliability, and maintainability. Lead technical decisions, investigate complex issues, mentor engineers, and collaborate with product, AI, data, security, and engineering teams.

Lead complex multimedia technology programs from strategy through delivery, managing plans, milestones, metrics, quality, risks, releases, and continuous improvement. Partner with software engineering, product management, OEMs, and cross-functional stakeholders to resolve technical challenges and deliver audio and video solutions. Provide technical leadership, communicate program status and risks, influence without authority, and drive root-cause corrective actions. The role focuses on software and hardware lifecycles, system architecture, and strategic execution in accelerated computing.

Leads SAP EWM and Logistics Execution projects, including warehouse and shipment process design, configuration, integration, testing, reporting, training, and operational support. Partners with business and technical teams to translate requirements into SAP ECC and S/4HANA solutions integrated with TMS, finance, production, sales, planning, and external systems. Provides mentorship, drives roadmaps and change management, and supports transformational supply chain initiatives.

Design, develop, and deploy enterprise agentic AI applications using LLMs, RAG, and orchestration frameworks. Build scalable AI services, APIs, reusable components, and integrations with PLM, SAP, and other enterprise systems. Translate business needs into AI-driven workflow solutions, improve quality through prompt engineering and evaluation, and support the full software development lifecycle through production. Collaborate with business, IT, and engineering teams to deliver secure, reliable, cloud-native AI applications.

Design and evolve a scalable, modular software platform for autonomous vehicles and ADAS across multiple OEMs. Responsibilities include defining component boundaries, refactoring ADAS software, improving integration, establishing testing and architectural documentation, and profiling performance in multicore and real-time environments. The role requires close collaboration across software, systems, and hardware teams, with emphasis on C++, embedded operating systems, multithreaded systems, and performance-critical architecture.

Develop AI-driven automated tests, tools, utilities, and frameworks for NVIDIA GPU device drivers and SDKs on Windows. Analyze test results, isolate defects, perform root-cause analysis, improve code coverage, validate compatibility across products and interfaces, and support CI/CD testing. The role includes code reviews, test reporting, delivery prioritization, corrective actions, and collaboration with engineering leadership and cross-functional teams. This is a fully onsite position.

Develop AI-driven automated tests, tools, utilities, and frameworks for NVIDIA GPU device drivers and SDKs on Windows. Analyze test results, identify defects, perform root-cause analysis, measure code coverage, validate compatibility across hardware and interfaces, participate in code reviews, and report release readiness. The role also supports CI/CD testing, workflow improvement, corrective actions, task prioritization, and cross-functional collaboration.

Lead post-launch operations for Sales AI applications, ensuring availability, performance, security, governance, support readiness, and continuous improvement. Establish incident, problem, change, release, and lifecycle processes; monitor service and adoption metrics; develop automation, observability, self-service capabilities, and production-readiness practices. Partner with Sales, Product, Engineering, Data, Security, and IT teams to improve user experience, platform reliability, adoption, and business value.

Design, build, and evolve secure, scalable full-stack sales applications incorporating generative AI, intelligent workflows, APIs, data services, and cloud infrastructure. Lead architecture and technical design decisions, integrate AI models and retrieval systems, improve testing, CI/CD, observability, and production reliability, and mentor engineers. Collaborate with Product, AI/ML, Data, Security, Solution Architecture, and Engineering teams to deliver enterprise applications globally.

Develop and productize NVIDIA DRIVE OS software for automotive platforms. Responsibilities include shaping AUTOSAR architecture, evaluating software and hardware designs, prototyping, profiling, safety analysis, data analysis, and influencing next-generation architectures. The engineer will also resolve complex cross-team technical issues and work with AUTOSAR stacks, MCALs, CDDs, and software components across multiple microcontroller platforms.