Top Tech Jobs & Startup Jobs in Austin, TX

Lead performance characterization and optimization of large-scale AI workloads across GPUs, networking, storage, and software. Design and run benchmarks and profiling, diagnose bottlenecks, define evaluation methodologies and metrics, produce actionable optimization plans, and collaborate with DL engineers, platform teams, and GPU architects to deliver improvements.

Design and implement reliable, scalable full‑stack web tools (backend and frontend) to automate and optimize chip design and verification workflows. Build web applications in Go, TypeScript, and JavaScript, model hardware-level product definitions, research solutions to improve engineering efficiency, and support hundreds of global users.

Build and maintain automation flows and methodologies for VLSI interposer design. Develop tools and applications (Python/C++) to construct, analyze, and validate interposer designs, improve designer workflows, and integrate AI/ML-assisted techniques into EDA environments.

Design and implement production-grade VLSI/EDA tools and methodologies for multi-die package design. Build flows, applications, and automation to construct, analyze, validate, and optimize package designs to accelerate tape-out and scale engineering teams.

Develop and maintain automated build pipelines and DevOps tooling for NVIDIA compiler systems. Improve CI/CD workflows, support large developer teams, enhance release processes for predictability, automation, and reliability, and propose improvements for safety and compliance.

Develop silicon-accurate GPU kernel microbenchmarks, model-level performance analysis, and agentic kernel optimization to maximize LLM inference throughput and latency. Attribute bottlenecks across kernel, compiler, and runtime, produce optimization policies, and collaborate with compiler, hardware, kernel, and framework teams to deliver production-grade performance improvements.

As a Power Architect, you'll design GPU power features, collaborate with teams on power management strategies, and develop energy efficiency solutions using machine learning.

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.

Develop physical design methodologies for rail analysis and signoff, craft workflows for power and noise analysis, and provide creative solutions for IR analysis needed for NVIDIA chips.

Develop physical design methodologies for graphics processors and SOCs, focusing on innovative solutions to PPA problems and ML-based development.