Top Tech Jobs & Startup Jobs in Austin, TX

Design, build, and optimize GPU‑accelerated inference software and open‑source frameworks (vLLM, SGLang, FlashInfer). Improve performance and scale for LLMs and generative models across NVIDIA GPUs and edge SoCs using CUDA, CUTLASS, Triton, NCCL and profiling tools. Collaborate across teams to enable efficient model serving and deployment.

The Senior Formal Verification Engineer will verify sophisticated ASIC builds using formal methods, collaborate with architecture teams, develop verification environments, and support silicon debug.

Create and deliver developer-focused technical content and workshops that teach CUDA and NVIDIA GPU technologies. Evaluate new CUDA features, produce tutorials and blog posts, capture developer feedback to inform product decisions, and collaborate across product, engineering, and marketing teams to drive developer adoption.

Advise ISVs on designing, deploying, and optimizing large-scale accelerated AI infrastructure. Lead architecture reviews, POCs, benchmarks, and production deployment guidance across compute, networking, storage, containers, orchestration, observability, and CI/CD. Create reference architectures, sizing guidance, technical playbooks, demos, and whitepapers. Support cluster monitoring, reliability, and performance improvements. Up to 20% travel for customer engagements.

Serve as a customer-facing solutions architect for CAE/CFD/FEA ISVs and OEMs, accelerating simulation and digital-twin workflows on NVIDIA platforms. Advise on GPU-acceleration, physics-informed ML/surrogate modeling, Omniverse integration, architecture analysis, and deliver trainings, demos, and prototypes to improve performance and simulation cycles.

Lead invention of AI-driven ASIC development methodology, identify cross-stage bottlenecks, build scalable data pipelines, define metrics, mentor engineers, and evaluate AI/EDA research to improve predictability and cycle time.

Lead and scale an engineering team building GPU-accelerated deep learning inference frameworks. Drive strategy and roadmap, partner with compiler/libraries/research teams, oversee performance tuning and multi-GPU optimizations, mentor engineers, and deliver production-ready inference pipelines for LLM, multimodal, and generative AI across datacenter and edge platforms.

Lead product definition, execution, and go-to-market for AI physics models, libraries, and frameworks. Collaborate with engineering, developer relations, marketing, and sales; present to internal and external audiences; prioritize projects; translate technical capabilities into developer and customer value.

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.