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Founding GPU Engineer

2 months, 3 weeks ago
United Kingdom
Full-time
Mid Level
AI Engineer
Software Development
C++ Kubernetes Python
Apply Now
Fuse Energy

Fuse Energy

Fuse Energy is a leading UK electricity supplier prioritizing affordability, service excellence, and sustainability through renewable energy projects and global reinvestment efforts.

Renewable Electricity
11-50
Founded 2014
$78M raised
View All Jobs 16

Description

  • Design, implement, and optimize 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 utilization with real-time energy pricing and grid signals.
  • Optimize 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.
  • Collaborate with ML and systems engineers to integrate custom kernels into training and inference pipelines.
  • Benchmark against CPU and 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, including SMs, warps, memory hierarchy, and occupancy.
  • Proficiency in C++ and CUDA, with experience using Python for tooling and orchestration.
  • Experience with performance profiling tools such as Nsight Systems and Nsight Compute.
  • Familiarity with multi-GPU and multi-node scaling tools and concepts such as NCCL, MPI, RDMA, and InfiniBand.
  • Strong grasp of memory optimization, kernel fusion, and parallel algorithm design.
  • Comfort working across the stack from low-level kernels to system-level infrastructure.
  • Experience with Triton, cuDNN, cuBLAS, or custom ML inference and training frameworks is preferred.
  • Exposure to data center power or thermal management, or demand-response systems, is preferred.
  • Background in HPC, quantitative finance, or large-scale distributed systems is preferred.
  • Familiarity with Kubernetes or Slurm for GPU cluster orchestration is preferred.
  • Interest or experience in energy markets, grid systems, or sustainability-focused compute is preferred.

Benefits

  • Competitive salary and an equity sign-on bonus.
  • Biannual bonus scheme.
  • Fully expensed tech to match your needs.
  • Breakfast and dinner allowance for office-based employees.

Interested in this position?

Apply directly on the company website

Apply Now

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