Founding GPU Engineer

The Opportunity

Responsibilities

• Design, implement, and optimise CUDA kernels for high-throughput, latency-sensitive workloads.
• Profile and tune GPU performance across compute, memory bandwidth, and interconnect (NVLink/PCIe) bottlenecks.
• Build tooling to correlate GPU cluster power draw and utilisation with real-time energy pricing and grid signals.
• Optimise multi-GPU and multi-node scaling using NCCL, MPI, or similar communication libraries.
• Work with data center infrastructure teams on power capping, dynamic voltage/frequency scaling, and workload scheduling strategies that reduce energy cost and carbon intensity.
• Collaborate with ML/systems engineers to integrate custom kernels into training/inference pipelines.
• Benchmark against CPU/GPU baselines and drive continuous performance improvements.
• Contribute to internal libraries, documentation, and best practices for GPU performance engineering.

Requirements

• 4+ years of experience writing production CUDA code, or equivalent strong project/industry experience.
• Deep understanding of GPU architecture (SMs, warps, memory hierarchy, occupancy).
• Proficiency in C++ and CUDA; experience with Python for tooling/orchestration.
• Experience with performance profiling tools (Nsight Systems/Compute).
• Familiarity with multi-GPU/multi-node scaling (NCCL, MPI, RDMA/InfiniBand).