Top Tech Jobs & Startup Jobs in Austin, TX

Drive physical design for CPUs, GPUs, and SoCs focusing on netlist-related tasks, logic synthesis, and timing closure. Help in ECO generation.

Develop Pythonic CUTLASS DSL infrastructure and APIs for high-performance GPU programming. Build AST transformations, JIT compilation, debugging and profiling tools, validation systems, package distribution, documentation, and testing. Collaborate on open-source framework development while optimizing developer experience and GPU kernel delivery.

Lead the optimization and performance analysis of distributed training and inference workloads on NVIDIA GPU platforms, with responsibilities including debugging, benchmarking, and ensuring reliability of large-scale AI systems.

Lead technical solution integration for strategic customers, cloud providers, and hyperscalers using NVIDIA AI infrastructure. Develop and deploy compute and networking solutions, support customer proofs of concept, conduct technical workshops and performance debugging, and advise business and engineering teams on product strategy. The role requires deep expertise in AI data-center networking, GPUs, InfiniBand, Ethernet, PCIe, switches, hosts, optics, and rack-scale infrastructure.

Design, develop, test, and optimize system software for next-generation ARM-based SoCs across pre- and post-silicon phases. Analyze architectural bottlenecks, workload performance, and performance-per-watt tradeoffs; develop hardware/software policies and optimization strategies. Lead performance analysis, competitive benchmarking, architecture documentation, and adoption of improvements across NVIDIA software products and open-source SDKs.

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. The role requires deep VLSI or computer architecture expertise and extensive ASIC physical design experience.

Develop power and energy modeling tools for next-generation GPUs, CPUs, and SoCs. Build data-generation, model-integration, analytics, visualization, and database infrastructure; optimize runtime and memory performance; analyze pre- and post-silicon data; collaborate with architecture, design, verification, emulation, and physical-design teams; and apply machine learning to guide chip power and energy targets.

Develop GPU memory-system architecture and microarchitecture for leading-edge silicon. Optimize performance, power, area, complexity, and schedule; build functional and performance models; analyze workloads and simulations; define experiments; create specifications; collaborate with industry partners; and debug issues using models, RTL simulation, and silicon results.

The role involves developing NvSci software for cross-platform integration, enhancing APIs, optimizing performance, and ensuring automotive safety standards, while leading complex system feature development.

Build AI-driven compiler intelligence for production compiler pipelines. Design compiler optimization workflows, develop learning-based decision systems, integrate LLMs with compiler infrastructure, and validate performance across workloads. Collaborate with compiler, architecture, and performance teams to deploy reliable, scalable solutions that improve runtime, compile time, and efficiency.