# Research Engineer - CUDA Kernel Engineering

**Company:** [Voltai](https://hotfix.jobs/companies/voltai)
**Location:** Palo Alto, CA
**Role:** Backend Engineering
**Skills:** CUDA, PyTorch, Nvidia Hopper, Nvidia Blackwell, Nvlink, Nccl, Triton Inference Server, Megatron, vLLM, Torchtitan
**Posted:** 2025-11-06

> Develops and optimizes CUDA kernels for AI models accelerating semiconductor design, verification, and chip optimization across large-scale GPU clusters. Integrates with frameworks like PyTorch and latest NVIDIA hardware for training, inference, and RL workloads.

## Job Description

## Responsibilities
- Develop, integrate, and optimize state-of-the-art **CUDA kernels** to power AI models accelerating semiconductor design and verification.
- Enable large-scale model training, inference, and reinforcement learning systems for circuit layouts, RTL generation/validation, and chip architecture optimization across thousands of GPUs.
- Build tools, performance benchmarks, and integration layers to push GPU utilization limits for compute-intensive workloads in AI-driven hardware design.
- Collaborate with researchers and engineers; contribute kernels and tooling to open-source AI and HPC ecosystems.

## Requirements
- Writing and optimizing **CUDA kernels** for large-scale AI workloads (attention, routing, graph-based operations, physics-inspired operators).
- Profiling and optimizing **GPU performance** for custom compute or memory-bound workloads.
- Integrating custom kernels into training and inference frameworks (**PyTorch**, **Megatron**, **vLLM**, **TorchTitan**).
- Working with latest **NVIDIA hardware/software** (**Hopper**, **Blackwell**, **NVLink**, **NCCL**, **Triton**).
- Building GPU-accelerated primitives for graph reasoning, symbolic computation, or hardware simulation.
- Collaborating with AI researchers and semiconductor experts to translate domain-specific workloads into high-performance GPU code.

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