Fuse Energy is a forward-thinking renewable energy startup on a mission to deliver a terawatt of renewable energy – fast.

We're combining first-principles thinking with cutting-edge technology to build a radically better energy system.

As data centers become one of the largest and fastest-growing sources of electricity demand, Fuse is expanding into high-performance compute infrastructure that sits at the intersection of energy and AI – optimising how power-dense GPU workloads are scheduled, cooled, and balanced against grid conditions in real time.

We're looking for a Founding GPU Engineer to develop and optimise GPU-accelerated software for data center systems.

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.

Requirements

• 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.