Jobs at NVIDIA

Leads a distributed team responsible for power profiling, measurement infrastructure, regression monitoring, battery-life validation, and release qualification across NVIDIA platforms. Owns instrumented labs, device fleets, power analytics, automated regression farms, energy-compliance evidence, and cross-company methodology alignment. Drives power analysis from pre-silicon modeling through production, resolves power gaps with hardware and software teams, and provides qualification data for product release decisions.

Develop kernel-level system software for NVIDIA hardware across the chip lifecycle, from architecture and development through production. Collaborate with hardware and cross-functional teams to solve complex performance and power-efficiency problems involving PCIe and connected technologies. The role requires deep system software expertise, C proficiency, kernel debugging skills, and strong technical leadership. Experience with SoC silicon bring-up, AI-assisted development, and broad system-level architecture is advantageous.

Drives power, performance, and performance-per-watt analysis for SoC and Windows on ARM systems. Builds automated power measurement infrastructure, analyzes telemetry and firmware traces, optimizes low-power states and battery-life scenarios, and collaborates with hardware, firmware, OS, and driver teams from pre-silicon modeling through post-silicon validation. Develops visualizations and reports to identify regressions and recommend system optimizations.

Develop and maintain board support package components for SoC and GPU platforms, including bootloaders, device drivers, and kernels. Bring up new SoC and GPU architectures, solve product-level issues, and integrate BSPs with OEM platforms. Collaborate across hardware and software teams while contributing to operating-system-agnostic embedded solutions. The role requires expertise in embedded systems, SoC architecture, and BSP development, with UEFI and EC experience preferred.

The role involves developing compilers for NVIDIA GPUs, working on optimization problems, collaborating with a distributed team, and influencing new GPU architectures.

Develop system software, tools, and infrastructure for validating and productizing next-generation graphics and computing processors. Collaborate with architecture, hardware, and driver teams; assess hardware features; create manufacturing diagnostics; and debug robust solutions using operating systems, algorithms, and computer architecture expertise.

Leads engineering strategy, architecture, delivery, and operations for NVIDIA’s AI-powered employee productivity platform. Manages and develops engineering teams building web, mobile, and agentic experiences at global scale. Oversees technical roadmaps, distributed systems, enterprise integrations, reliability, security, privacy, observability, and responsible AI practices while partnering with Product, Design, HR, Security, SRE, and business stakeholders.

Architects, implements, and supports advanced SAP IBP and APO supply chain planning solutions. Leads process improvements, prototypes, testing, upgrades, training, data analysis, and optimization modeling for supply and demand planning. Collaborates with business and IT teams to deliver scalable solutions, improve profitability, and support semiconductor industry planning needs. The role also mentors stakeholders and influences senior leadership on system adoption and operational improvements.

Develop and maintain NVIDIA GeForce NOW’s native Android streaming stack across input, networking, video rendering, UI, packaging, and distribution. Optimize latency, quality, performance, reliability, and security across diverse devices and operating systems. Lead performance benchmarking, profiling, debugging, feature design, technical documentation, delivery planning, and cross-team coordination. Contribute to code reviews and collaborate with engineering, QA, and external Android partners.

Modify and improve GCC, LLVM, and proprietary compilers for security and code hardening. Analyze source and assembly code, address vulnerabilities, implement countermeasures, and coordinate security measures with compiler, hardware, and application teams. The role also involves distributed systems, full-stack development, databases, caching, containerization, orchestration, compiler technology, and potentially LLM-based security testing and offensive evaluation.

Develop Python-based automation frameworks, tests, and tools for NVIDIA App across Windows and Linux. Integrate APIs and AI-assisted, multi-agent systems to accelerate validation, improve test coverage, and identify defects. Translate requirements into test plans, debug issues across software, hardware, drivers, games, and operating systems, and collaborate with development and QA teams to improve product quality.

Develop Python-based test frameworks and CI/CD automation for NVIDIA Metropolis AI and video analytics workflows. Build functional, integration, system, and end-to-end validation across cloud, data center, workstation, and edge platforms. Validate distributed microservices, GPU deployments, video search and summarization, AI agents, and computer vision pipelines. Benchmark accuracy, latency, scalability, and reliability while using Docker, Kubernetes, Helm, and AI-powered tools to improve testing, debugging, coverage, and regression analysis.

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.

Supports and operates NVIDIA’s large-scale on-premises and cloud network infrastructure in a 24/7 global operations environment. Responsibilities include monitoring alerts, resolving incidents within SLAs, troubleshooting network issues, managing IP infrastructure and datacenter interconnects, coordinating vendors, implementing upgrades and capacity expansions, improving operational workflows, and documenting best practices. The role requires extensive networking protocol knowledge, cloud experience, automation exposure, and strong incident-management and communication skills.

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.