Founding GPU Engineer

Fuse Energy

12h

About The Position

Fuse Energy is an energy startup on a mission to make energy abundant and affordable, fast. We combine first-principles thinking with cutting-edge technology to build a radically better energy system. We've raised over $200M from top-tier investors including Balderton, Lakestar, Accel, Creandum, Lowercarbon, Ribbit, 20VC, Hummingbird and Collaborative Fund, alongside strategic angels including Nico Rosberg and GPs behind Meta, Revolut, Spotify and Uber. We're building a fully integrated energy company: developing our own solar, batteries and other generation projects, building our own hardware, improving and developing grid infrastructure, trading power in real time, using AI across the business, and installing distributed energy in homes. By selling directly to consumers we cut out the middleman, lower costs and pass the savings on to our customers. As data centres become one of the largest and fastest-growing sources of electricity demand, Fuse is expanding into high-performance compute infrastructure 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 centre systems: low-level performance engineering for large-scale compute clusters, tying GPU workload behaviour to energy availability and grid demand. This puts CUDA/GPU performance engineering at the centre of how Fuse scales its compute infrastructure.

Requirements

4+ years 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 Haves

Triton, cuDNN, cuBLAS or custom ML inference/training frameworks
Data centre power/thermal management or demand-response systems
HPC, quantitative finance or large-scale distributed systems
Kubernetes/Slurm for GPU cluster orchestration
Interest in energy markets, grid systems or sustainability-focused compute

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