5-7 years of experience in GPU programming and optimization
Proficient in writing CUDA kernels for large-scale AI applications
Experience with profiling GPU performance for compute or memory-bound tasks
Familiarity with integration of custom kernels into frameworks like PyTorch and Megatron
Knowledge of NVIDIA hardware and software ecosystems
Ability to develop GPU-accelerated solutions for diverse computational problems
Responsibilities
Develop and optimize state-of-the-art CUDA kernels for AI semiconductor design
Integrate GPU kernels into training and inference systems
Create performance benchmarks and tools for GPU utilization in AI workloads
Collaborate with engineers and researchers in semiconductor and AI fields
Contribute optimized kernels to the open-source AI and HPC communities
Benefits
Work alongside a diverse team of industry-leading experts
Opportunity to contribute to cutting-edge AI and semiconductor research
Access to state-of-the-art NVIDIA hardware and software
Unique chance to impact the future of semiconductor design through AI
Collaboration with top academic institutions and industry investors
Full Job Description
About the Role
You will develop, integrate, and optimize state-of-the-art CUDA kernels to power AI models that accelerate semiconductor design and verification. Your work will enable large-scale model training, inference, and reinforcement learning systems that reason about circuit layouts, generate and validate RTL, and optimize chip architectures - running efficiently across thousands of GPUs. You'll build tools, performance benchmarks, and integration layers that push the limits of GPU utilization for compute-intensive workloads in AI-driven hardware design. Working closely with researchers and engineers, you'll help make Voltai the world's leading AI + semiconductor research organization. You'll also release your kernels and tooling as contributions to the open-source AI and HPC ecosystems.
You might thrive in this role if you have experience with
Writing and optimizing CUDA kernels for large-scale AI workloads (attention, routing, graph-based operations, physics-inspired operators, etc.)
Profiling and optimizing GPU performance for custom compute or memory-bound workloads
Integrating custom kernels into cutting-edge training and inference frameworks (e.g., PyTorch, Megatron, vLLM, TorchTitan)
Working with the latest NVIDIA hardware and software stacks (Hopper, Blackwell, NVLink, NCCL, Triton)
Building GPU-accelerated primitives for graph reasoning, symbolic computation, or hardware simulation tasks
Collaborating with AI researchers and semiconductor experts to translate domain-specific workloads into high-performance GPU code
