Top Tech Jobs & Startup Jobs in Austin, TX

Designs and deploys large-scale NVIDIA AI and HPC GPU infrastructure for cloud partners and strategic customers. Leads technical engagements across the customer lifecycle, supports infrastructure development and production, builds networking and compute proofs of concept, troubleshoots performance and scaling issues, and delivers technical presentations and workshops. Partners with sales and business teams to identify opportunities and provides customer feedback to influence product strategy.

Design and implement LLM-first APIs and documentation for CUDA-X math libraries. Develop tools, metrics, and processes to improve AI-agent code generation and transform library APIs into LLM-friendly formats. Collaborate with stakeholders on LLM-enhanced releases while prototyping solutions for code-generation platforms. Requires expertise in LLMs, fine-tuning, reinforcement learning, RAG, MCP, agent tooling, composable API design, and technical documentation.

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.

Lead an engineering team responsible for reliability processes and platforms, including incident management, maintenance, on-call, issue management, and production readiness. Own the roadmap, technical direction, delivery, adoption, and continuous improvement of operational systems. Partner across infrastructure, product, and security teams; lead major incident response; hire and develop engineers; and apply automation, AI, and operational insights to improve service reliability and reduce manual work.

Lead and grow a software engineering team responsible for CUDA Math Libraries platform expansion and readiness. Own integration, qualification, compliance, validation, performance testing, defect triage, and technical roadmaps across specialized platforms. Collaborate with library, DevOps, QA, compiler, CUDA, release, and platform teams. The role requires hands-on high-performance numerical software development using C++ and Python, parallel computing, performance optimization, and software engineering leadership.

Develop and support NVIDIA CUDA Math Libraries across emerging and specialized platforms. Responsibilities include platform integration, functional qualification, defect triage, bug fixing, performance benchmarking, regression resolution, and building CI/CD qualification pipelines. The role requires high-performance numerical software development using C++ and Python, parallel computing, numerical methods, and software architecture, with preferred experience in CUDA, GPU computing, math libraries, cross-platform development, and embedded or safety-critical systems.

Develop and productize machine learning models for autonomous vehicle perception, including LiDAR and camera perception, sensor fusion, detection, classification, segmentation, and tracking. Build scalable data, training, metrics, and performance workflows, and implement efficient C++ code for the NVIDIA DRIVE AV platform. Collaborate across engineering teams, track advances in machine learning, and optimize models for real-time embedded inference.

Owns the segment sales strategy and revenue growth for NVIDIA DOCA and BlueField infrastructure software. Builds pipelines, forecasts, customer and partner plans, and commercialization paths across networking, cybersecurity, storage, cloud infrastructure, and data services. Develops lighthouse customers and ecosystem partnerships, shapes complex pricing and licensing opportunities, leads executive and technical discovery, and enables sellers and partners to communicate measurable customer value. Requires extensive enterprise infrastructure software sales, business development, technical sales, and partner go-to-market experience.

Build and operate software automation for large-scale NVIDIA GPU infrastructure. Responsibilities include bare-metal provisioning, hardware validation, firmware upgrades, repair workflows, cluster lifecycle management, BMC and Redfish tooling, and NVIDIA NVL72 and BlueField DPU operations. Diagnose failures across hardware, Linux, networking, and Kubernetes; support incident response, on-call operations, observability, root-cause analysis, and permanent remediation across cloud and on-premises environments.

Lead technical engagements with OEM and deployment partners to design, validate, and deploy scalable NVIDIA AI infrastructure. Map customer workloads to NVIDIA hardware and software, co-design architectures, resolve complex technical issues, drive platform adoption, and influence product priorities. Lead AI Factory build-readiness execution, deployment validation, technical enablement, and issue resolution across large-scale GPU infrastructure deployments. Travel up to 50% is required.