[Hiring] Founding GPU Engineer @Fuse Energy

Role Description

Fuse is looking for a Founding GPU Engineer to develop and optimise GPU-accelerated software for data center systems. You'll work on low-level performance engineering for large-scale compute clusters, helping Fuse build the software layer that ties GPU workload behaviour to energy availability and grid demand.

The Opportunity:

• Fuse is in active discussions with major AI compute customers who need data center capacity across the markets we operate in, primarily for inference.
• Demand significantly outpaces what we can currently build, meaning speed to power, reliability, and deployment cost matter more than specific hardware choice.
• This puts CUDA/GPU performance engineering at the center of how Fuse serves some of the largest compute buyers in the market.

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.

Qualifications

• 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).
• 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, cuDNN, cuBLAS, 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.

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.