Top Tech Jobs & Startup Jobs in Austin, TX

Operate and improve large-scale Kubernetes and GPU clusters across public and private clouds. Build automation, observability, capacity-management, and reliability systems; define SLOs and SLIs; support production launches; lead incident triage and root-cause analysis; conduct blameless postmortems; and participate in on-call support for AI workloads.

Develop and lead production software-defined networking for NVIDIA’s global GPU cloud infrastructure. Design multi-tenant control and data planes using Open vSwitch, OVN, OpenFlow, Kubernetes, C, and Go. Build networking services, plugins, automation, testing, observability, and reliability improvements. Lead architecture and code reviews, mentor engineers, support on-call operations, and resolve complex production incidents through diagnostics and source-level debugging.

The Senior Production Engineer will scale AI infrastructure, manage production systems, implement monitoring capabilities, and ensure reliability and performance across NVIDIA's AI clusters.

Design, build, and deploy LLM-powered validation pipelines and AI integrations for silicon design and post-silicon validation. Lead cross-team AI projects from prototype to production, evaluate emerging AI tech, and measure impact with data systems to improve workflows and scalability across chip development.

Develops strategic relationships and revenue growth with universities and higher-education research institutions. Owns the complete sales cycle, from prospecting and discovery through negotiation and closing, while meeting quotas and forecasting revenue. Creates territory and account plans, translates complex software and computing requirements into NVIDIA solutions, and collaborates with solution architects, product teams, developer relations, business units, and channel partners. Requires consultative technology sales expertise and understanding of AI, accelerated computing, and research environments.

Design, build, and operate core cloud-native infrastructure services for NVIDIA DGX Cloud. Develop scalable, secure microservices and automation for provisioning and lifecycle management, integrate internal and external platforms, and implement observability and security to ensure reliable, global production operations.

Leads complex customer engagements for NVIDIA infrastructure services from planning through deployment, bring-up, support handover, and day-two readiness. Coordinates solution architects, delivery managers, sales, OEMs, operations, and support teams; provides executive status updates; manages risks, issues, blockers, scope, and commercial commitments; and drives resolution of critical customer and delivery concerns while maintaining technical oversight.

Research and develop GPU-acceleration techniques for high-performance databases, ETL, and data analytics applications. Analyze data-intensive workloads, optimize algorithms, memory management, and CUDA kernels, and investigate CPU/GPU bottlenecks. Collaborate with research, hardware, software, and industry teams to influence next-generation architectures, programming models, and data-processing solutions.

Design, develop, test, and extend NVIDIA’s large-scale object storage platform. Responsibilities include improving availability, reliability, durability, fault tolerance, and performance across deployments exceeding 10,000 nodes and exabytes of data. The role also automates storage infrastructure provisioning, management, and monitoring; supports AI/ML research teams; owns product delivery through support; and contributes to open-source storage projects.

Build and maintain network automation and orchestration software for NVIDIA GPU Cloud and SuperPod deployments. Design/configure centralized configuration management, integrate cloud provider APIs, implement data pipelines and streaming telemetry, and automate lifecycle operations from procurement to deployment. Collaborate with infrastructure experts to deliver zero-touch, production-grade network provisioning, monitoring, and management tools.