Founding GPU Engineer - Equity & Biannual Bonus

Fuse Energy; Greater London; On-site; GBP 90,000 - 130,000; Full time

Benefits offered by this job

Equity sign-on bonus

Biannual bonus

Fully expensed tech

Breakfast and dinner allowance

Job summary

Fuse Energy is seeking a seasoned CUDA performance engineer to design and optimise kernels for high-throughput workloads in a high-performance compute environment.

You will profile GPU bottlenecks, build tooling to align power draw with energy pricing, and optimise multi-GPU scaling across NCCL/MPI. Collaboration with ML and systems teams is essential to improve training and inference pipelines.

Qualifications

• 4+ years of experience writing production CUDA code or equivalent.
• Deep understanding of GPU architecture (SMs, warps, memory hierarchy).
• Proficiency in C++ and CUDA; Python for tooling/automation.
• Experience with performance profiling tools (Nsight Systems/Compute).
• Familiarity with multi-GPU/multi-node scaling (NCCL, MPI, RDMA/InfiniBand).
• Strong memory optimization, kernel fusion, and parallel algorithm design.
• Comfortable from kernel to system-level infra.

Responsibilities

• Design, implement, and optimise CUDA kernels for high-throughput workloads.
• Profile GPU performance across compute, memory, and interconnect bottlenecks.
• Build tooling to correlate GPU power draw with energy pricing and grid signals.
• Optimise multi-GPU/multi-node scaling using NCCL, MPI, or similar libraries.
• Collaborate with data center teams on power capping and DVFS strategies.
• Integrate custom kernels into ML training/inference pipelines.
• Benchmark against CPU/GPU baselines and drive improvements.
• Contribute to internal libraries, docs, and best practices for GPU performance.

Skills

CUDA programming

C++

Python tooling

GPU architecture knowledge

Performance profiling

Multi-GPU scaling

Memory optimization

Energy-aware compute

Tools

Nsight Systems

NCCL

MPI

RDMA/InfiniBand

Job description

Fuse Energy is seeking a seasoned CUDA performance engineer to design and optimise kernels for high-throughput workloads in a high-performance compute environment.

You will profile GPU bottlenecks, build tooling to align power draw with energy pricing, and optimise multi-GPU scaling across NCCL/MPI. Collaboration with ML and systems teams is essential to improve training and inference pipelines.