Founding GPU Engineer

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.

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