Top Tech Jobs & Startup Jobs

Leads NVIDIA’s AI Cluster Runtime product, setting direction for a validated, reproducible GPU Kubernetes software stack. Expands support across accelerators, clouds, and AI workloads; oversees version-locked recipes, supply-chain provenance, and reliable deployment. Represents the project to customers, partners, and the open-source community while driving community adoption. Requires deep Kubernetes GPU infrastructure expertise, GitOps configuration delivery experience, and a strong open-source contribution record.

Build and operate AI infrastructure systems, including telemetry pipelines, observability dashboards, incident-management automation, hardware and software catalogs, service ownership data models, and self-service discovery tools. Lead cross-functional infrastructure initiatives and develop AI-assisted operational tooling. The role requires production software and infrastructure expertise, programming experience, and the ability to improve reliability, visibility, incident response, and operational efficiency across NVIDIA GPU cloud environments.

Develop and optimize deep learning and machine learning techniques for current and next-generation NVIDIA GPU architectures. Provide GPU-based AI solutions to key customers and collaborate with architecture, research, libraries, tools, and systems software teams to shape future computing platforms and programming models. The role emphasizes parallel programming, performance optimization, AI algorithms, and low-level computer architecture.

Design and operate large-scale infrastructure supporting GPU- and CPU-based EDA workloads. Build automation for provisioning, configuration, deployment, lifecycle management, monitoring, health remediation, cluster enrollment, and recovery across Linux compute fleets. Integrate services with schedulers, infrastructure systems, and observability platforms. Participate in incident response, root-cause analysis, capacity planning, and reliability improvements while collaborating with EDA, networking, storage, and hardware teams.

Design and optimize ASIC, SoC, and GPU floorplans during early chip development. Collaborate with architecture, physical design, packaging, and ASIC teams to improve area, interconnect, timing, routing, power, and clock distribution. Drive area reviews, resolve congestion and timing issues, and develop tools and infrastructure to improve chip design efficiency and execution speed.

Build and lead development of NVIDIA’s open-source NeMo Core Platform for executing, evaluating, deploying, and optimizing agentic AI systems across local, Docker, Kubernetes, Slurm, on-premises, and air-gapped environments. Design platform APIs, plugins, SDKs, job orchestration, authentication, RBAC, observability, testing, and reliability features. Provide technical leadership through architecture and code reviews, mentoring, and ownership of complex cross-component problems.

Owns customer relationships and revenue growth for NVIDIA’s supercomputing, HPC, and AI products across federal government and academic accounts. Responsibilities include demand creation, co-selling, forecasting, strategic account development, partner collaboration, go-to-market execution, customer education, and communicating customer-driven product strategy. The role focuses heavily on DOE labs, federal purchasing processes, long-term technology partnerships, and ecosystem expansion, with travel up to 50%.

Design, build, test, deploy, and support scalable software systems for NVIDIA cloud infrastructure. Develop network and systems automation for multi-tenant environments, manage APIs and distributed services, respond to operational events, contribute to open-source projects, and present technical roadmaps and demos to stakeholders and leadership.

Improve performance, reliability, and scaling of a distributed system that routes AI workloads to GPU fleets. Design and ship services in Java/Go/Rust, automate CI/CD, integrate with NVIDIA technologies, optimize build/test/release processes, and steward the open-source project by triaging issues, reviewing PRs, and writing contributor-facing docs.

Lead incident response, threat hunting, cloud and product security investigations across corporate, cloud, and product environments. Conduct digital forensics, malware analysis, vulnerability investigations, and workload analysis across AWS, Azure, GCP, Kubernetes, and containers. Develop detection strategies and security automation, guide engineering and business teams, and improve security policies and incident handling using threat models and MITRE ATT&CK.