Top Tech Jobs & Startup Jobs in Austin, TX

Serve as the senior technical contact for OEM Federal deployments of NVIDIA GPU-accelerated platforms. Own customer issues end to end, including troubleshooting, failure analysis, RMA procedures, root-cause investigation, and coordination with OEM support and NVIDIA engineering. Support secure Federal environments such as FedRAMP, IL5/IL6, and air-gapped SCIF facilities while maintaining sanitization and chain-of-custody requirements. Analyze multi-GPU systems, firmware, operating systems, networking, storage, and distributed workloads, with up to 35% travel.

Design and build intelligent compiler systems, compiler-oriented agents, code transformation workflows, and numerical verification infrastructure. Develop systems for low-level IR reasoning, code generation, optimization, differential testing, regression detection, and correctness validation across GPU-centric workloads. Collaborate with compiler, CUDA, runtime, library, and hardware teams to integrate capabilities into NVIDIA products while optimizing performance, scalability, and numerical correctness.

Develop compiler optimization algorithms for deep learning workloads, optimizing JAX and OpenXLA performance on NVIDIA GPUs. Design graph partitioning, tensor sharding, code generation, and performance-tuning solutions for distributed training and inference. Collaborate with deep learning framework and GPU hardware teams, implement user-facing JAX features, and contribute to production-grade software using MLIR, LLVM, and OpenAI Triton.

Develop and debug cloud-native solutions for AI/ML datacenters, focusing on Kubernetes and Slurm enhancements and customer workflows.

Develop GPU-accelerated, PyTorch-based machine learning solutions, including Graph Neural Networks, foundation models, and ensemble models. Build and optimize distributed training and inference workflows, support CUDA-X integrations, improve high-performance code, collaborate with customers and cross-functional teams, and provide technical leadership and mentorship.

Verify fabric and interconnect IP blocks and full-chip SoC integrations using SystemVerilog and UVM. Develop constrained-random and directed testbenches, create verification plans, validate DMA, routing, arbitration, ordering, flow control, and protocol compliance, and collaborate across architecture, design, software, emulation, and post-silicon teams. Drive performance, silicon, and platform verification strategies for complex interconnects and NoC subsystems.

Leads NVIDIA’s Infrastructure Security Engineering organization for EDA clusters. Builds and operates security controls across compute, storage, and networking in on-premises and multi-cloud environments while preserving performance for long-running workloads. Owns the technical roadmap, security technology lifecycle, automation, instrumentation, compliance evidence, and cross-functional execution. Develops secure-by-default infrastructure and leads teams responsible for scalable enforcement, observability, mitigation, and security outcomes.

Manage NVIDIA’s global Lenovo OEM account and drive adoption and integration of NVIDIA accelerated computing platforms. Develop technical expertise across AI hardware, networking, software, and reference architectures; identify solution opportunities; package NVIDIA technologies into Lenovo offerings; and develop co-marketing and co-selling strategies. Coordinate product, engineering, supply planning, pricing, quality, and go-to-market teams while building relationships with senior stakeholders and supporting cloud and enterprise sales channels.

Own and evolve Kubernetes networking for GPU superclusters spanning multiple clouds and thousands of nodes. Design and operate CNI data planes, overlay meshes, VPNs, gateways, load balancers, and tunnels. Identify scale limitations, build network validation environments, diagnose distributed networking failures, and partner with cloud providers on topology. Provide technical leadership while using strong Linux networking, systems programming, and large-scale debugging expertise.

Leads a globally distributed customer engineering organization supporting internal EDA infrastructure while overseeing a Kubernetes platform team. Responsibilities include developing managers, improving support operations, defining SLOs, translating support trends into engineering roadmaps, automating recurring operational work, and advancing AI/LLM-based support assistants. The role requires hands-on technical engagement with Kubernetes, EDA job schedulers, internal LLM infrastructure, and production environments.