Research Engineer - CUDA Kernel Engineering

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