Top Tech Jobs & Startup Jobs in Austin, TX

Develop and optimize high-performance math kernels for NVIDIA GPUs using CUTLASS, focusing on Tensor Core-based deep learning kernels in CUDA C++ and Python.

The role involves leading AI networking system transformations, collaborating with teams on product features, and managing customer engagements for innovative networking solutions.

Responsible for analyzing architectural requirements, delivering high-performance RTL blocks, collaborating with engineers, and assisting in IP solution integration.

Verify GPU micro-architecture using formal verification tools: define scope, write and implement formal testplans (assumptions, assertions, covers), develop abstractions to obtain proofs, drive tool performance, debug RTL, create scripts/flows, and communicate coverage to partners.

The role involves bridging architecture and design, analyzing power trade-offs, and improving power efficiency for CPU cores, with a focus on AI and HPC workloads.

Lead cross-disciplinary software architecture for next-generation CPUs and SoCs, collaborating with hardware, software, ASIC, verification, and platform teams. Influence hardware decisions via deep system software, OS/driver, and performance expertise, advise partners and senior management, and drive optimized solutions for AI and HPC workloads.

Design hardware accelerators and processors, develop power metrics, and collaborate with teams to deliver efficient, high-performance RTL.

Develop and deliver system-level IP and RTL to measure performance on GPUs/SOCs. Create and automate flows, run/debug RTL checks (CDC, clocks, resets, latency), implement RTL features, and collaborate with architects, designers, and software engineers to support projects and performance monitoring.

Lead floorplan design from early architecture through physical implementation for high-performance SoCs/CPUs/GPUs. Collaborate with cross-functional teams to optimize area, power, timing, and interconnect; develop AI-driven workflows and tools; identify technical risks and drive mitigation to meet product goals.

Design and scale production infrastructure for LLM-powered agents and copilots. Build reliable, high-performance services, APIs, CI/CD, and integrations; collaborate with research and hardware teams; mentor peers and drive continuous innovation in agentic AI applications.