Founding GPU Engineer

Overview

As Founding GPU Engineer, you will architect and optimize CUDA-based software for data center AI workloads. You’ll connect GPU performance with energy availability and grid signals, enabling efficient, scalable compute at scale. You’ll work across low-level kernels to system-level infrastructure with cross-functional teams. This role offers a chance to shape a mission-driven energy and AI platform with real-world impact. You help drive performance and energy efficiency at the physics of data centers.

Pay / Benefits

• competitive salary and an equity sign-on bonus
• Biannual bonus scheme
• Fully expensed tech
• Breakfast and dinner allowance

Responsibilities

• Design, implement, and optimise CUDA kernels for high-throughput, latency-sensitive workloads
• Profile and tune GPU performance across compute, memory bandwidth, and interconnect 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 libraries
• Collaborate with data center infrastructure teams on power capping and dynamic voltage/frequency scaling
• Integrate custom kernels into training/inference pipelines with ML/systems engineers
• Benchmark against CPU/GPU baselines and drive continuous performance improvements
• Contribute to internal libraries, documentation, and best practices for GPU performance engineering

Key requirements

• 4+ years of experience writing production CUDA code
• Deep understanding of GPU architecture (SMs, warps, memory hierarchy, occupancy)
• Proficiency in C++ and CUDA; Python for tooling
• Experience with performance profiling tools (Nsight Systems/Compute)
• Familiarity with multi-GPU/multi-node scaling (NCCL, MPI, RDMA/InfiniBand)
• Strong grasp of memory optimisation, kernel fusion, and parallel algorithm design
• Comfortable working across the stack from low-level kernels to system-level infrastructure

• CUDA
• C++
• Python
• Nsight Systems/Compute
• NCCL
• MPI

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