At a Glance
- Tasks: Design and optimise CUDA kernels for high-performance GPU workloads.
- Company: Join a cutting-edge tech company focused on sustainable compute solutions.
- Benefits: Competitive salary, equity bonus, tech allowance, and meal perks.
- Other info: Dynamic team environment with opportunities in energy markets and sustainability.
- Why this job: Be at the forefront of GPU performance engineering and make a real impact.
- Qualifications: 4+ years in CUDA coding, strong GPU architecture knowledge, and C++ proficiency.
The predicted salary is between 70000 - 90000 £ per year.
The Opportunity
Demand for high-performance compute capacity across the markets we operate in significantly outpaces what we can currently build, meaning speed to power and reliability are critical to how we scale.
This puts CUDA/GPU performance engineering at the center of how Fuse scales its compute infrastructure.
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.
- 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/Infini Band).
- Strong grasp of memory optimisation, kernel fusion, and parallel algorithm design.
- Comfortable working across the stack from low-level kernels to system-level infrastructure.
- Nice to Have
- Experience with Triton, cu DNN, cu BLAS, or custom ML inference/training frameworks.
- Exposure to data center power/thermal management or demand-response systems.
- Background in HPC, quantitative finance, or large-scale distributed systems.
- Familiarity with Kubernetes/Slurm for GPU cluster orchestration.
- Interest or experience in energy markets, grid systems, or sustainability-focused compute.
- 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.
- #J-18808-Ljbffr
Founding GPU Engineer employer: Fuse Energy
Fuse Energy is an exceptional employer, offering a dynamic work environment where innovation meets opportunity. As an AI Compute Data Centre Engineer, you will be at the forefront of cutting-edge technology in the UK, with access to competitive salaries, biannual bonuses, and fully expensed tech. Our culture fosters professional growth and collaboration, ensuring that every team member can thrive and contribute to impactful projects.
