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R&D intern developing and validating compiler features for high-performance computing. Responsibilities include analyzing CPU/GPU performance, reading PTX and LLVM assembly, implementing hardware intrinsics and mathematical algorithms, fixing compiler bugs, creating benchmarks and demos, and writing technical documentation. The role requires strong C++ or C# skills, understanding of memory management and object-oriented programming, and interest in CUDA, OpenMP, GPU architecture, algorithms, and numerical methods.

Lead customer-facing HPC deployments and serve as the escalation point for complex hardware, operating system, application, and compiler issues. Deploy and debug GPU-accelerated Kubernetes clusters across on-premises and cloud environments, using Python, C#/.NET, CUDA, and ROCm. Diagnose issues involving NVIDIA and AMD GPUs, high-speed interconnects, PCIe topology, and driver stacks. Create technical documentation, reproduce customer failures, and collaborate with engineering and R&D on bug reports and product improvements.

Own and develop HPC/GPU infrastructure for R&D and customer deployments. Design cross-platform CI/CD pipelines, manage on-premise and cloud Kubernetes clusters with GPU passthrough and MIG, configure self-hosted GitHub Actions runners, and maintain DockerHub registries. Assemble and tune physical GPU servers, manage PCIe topology and hardware constraints, and maintain compatibility across NVIDIA drivers, CUDA, and ROCm versions. Provide customer-facing technical assistance for GPU container deployments.

Develop compiler and high-performance computing solutions that translate .NET and Java bytecode into optimized CUDA and ROCm code for CPUs and GPUs. Analyze customer algorithms, design parallel implementations, extend compilation infrastructure, build reusable libraries, and optimize throughput and latency. Apply expertise in GPU programming, compiler architecture, mathematics, and software engineering to deliver performance matching or exceeding hand-tuned binaries.