Top Tech Jobs & Startup Jobs in Austin, TX

Partner with architecture and design teams to plan and execute functional and performance verification for GPU memory subsystem IP. Develop testbenches, BFMs, monitors and regressions, close coverage, enable FPGA/software testing, and support post-silicon verification and debug to sign off RTL.

The ASIC Design Efficiency Engineer will develop innovative hardware and system designs, focusing on performance, power, and area improvements, while collaborating with cross-functional teams.

Lead technical partnership with Alphabet teams to drive adoption and integration of NVIDIA libraries, models, and frameworks. Coordinate cross-functional engineering and GTM teams, resolve integration challenges, influence product roadmaps, and deliver joint solutions that accelerate partner workloads and developer adoption.

Lead design and implementation of agentic AI systems to automate data-science pipelines—data elicitation, curation, experimentation, model training/evaluation, diagnostics, and benchmarks. Collaborate with software and solutions teams and integrate solutions into enterprise projects and products.

Lead performance practices for large-scale AI systems, enhance datacenter products, and resolve complex performance issues through collaboration and technical expertise.

Design and implement an LLVM-based JIT backend for NVIDIA GPUs, develop compiler passes and optimizations to meet performance and compile-time targets, collaborate with hardware and application teams to resolve issues, apply modern compiler technologies, and influence future GPU architectures.

The Solutions Architect will engage with customers and partners to deliver solutions utilizing NVIDIA technology, focusing on AI, HPC, and data science, and educating stakeholders throughout the process.

The Senior Production Engineer will scale AI infrastructure, manage production systems, implement monitoring capabilities, and ensure reliability and performance across NVIDIA's AI clusters.

The role involves architecting and operating NVIDIA's cloud-native attestation platform, developing high-availability services, collaborating with cross-functional teams, and mentoring engineers.

Profile, benchmark, and optimize large-scale LLM training and inference across GPUs, CPUs, HCAs, and switches. Focus on collective communication and high-performance networking, build PyTorch trace-based profiling and replay tools, define performance test plans, and collaborate across hardware and software teams to identify bottlenecks and achieve performance targets.