Top Tech Jobs & Startup Jobs in Austin, TX

Develop and maintain scalable full-stack web applications, web services, microservices, and large-scale data platforms supporting VLSI design and modeling. Responsibilities include frontend and backend development, requirements consultation, performance analysis, database and object modeling, and infrastructure development for big-data scenarios.

Develop and optimize C++ algorithms and tools for VLSI physical design, including chip placement, floorplanning, routing, computational geometry, graph optimization, and design-space exploration. Build machine-learning strategies, visualization capabilities, and GUIs that improve chip performance, power, and area. Collaborate with design teams to deploy high-performance software for next-generation AI hardware.

You will integrate communication libraries into AI frameworks, analyze workloads, improve AI compilers, and author custom kernels for performance.

The role involves validating clocking structures in products, developing test plans, automation, and partnering with design teams to ensure high-quality verification of ASIC designs.

Develop innovative physical design methodologies for GPUs and SOCs, focusing on PPA improvements and collaboration across multiple teams. Use AI/ML to enhance design efficiency.

Architect and maintain profiling frameworks for CPU and GPU environments, benchmark EDA applications, analyze memory and kernel performance, identify bottlenecks, optimize memory usage, and develop predictive models for hardware procurement and cloud instance selection.

Own verification strategy, planning, execution, testbench development, stimulus and coverage for sophisticated CPU designs. Apply SystemVerilog and UVM methodologies, collaborate with architecture and build teams, debug hardware and software issues, track progress toward tape-out, and mentor verification engineers.

Design and optimize multi-agent AI systems for NVIDIA’s CUDA ecosystem, including agent runtimes, compiler-integrated orchestration, evaluation frameworks, and GPU-centric performance solutions. Collaborate across hardware, software, algorithms, compilers, kernels, frameworks, distributed training, and inference teams to accelerate agent planning, tool use, code generation, and other AI workloads.

Design and implement an LLVM-based backend JIT compiler for NVIDIA GPUs. Develop compiler passes and optimizations, focusing on high performance and low compile time. Collaborate with hardware, compiler, and application teams to resolve issues and influence future GPU architectures.

Develop compiler optimization algorithms for deep learning networks and AI inference platforms. Collaborate with software framework and GPU architecture teams to improve performance, build times, memory usage, APIs, and compiler techniques for current and future NVIDIA GPUs. Analyze performance, implement and test compiler technologies, support CPU/GPU workloads, and independently lead development efforts.