CUDA Engineer

Fuse Energy

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. We're looking for a CUDA Engineer to write and optimise the low-level GPU code that powers our inference workloads: designing custom CUDA kernels, tuning performance across memory bandwidth and compute bottlenecks, and squeezing maximum throughput out of every GPU in our fleet, working at the level of SMs, warps and memory hierarchies.

Requirements

4+ years writing production CUDA code, with a track record of shipping performance-critical kernels
Deep understanding of GPU microarchitecture: warps, occupancy, register pressure and memory hierarchy
Strong CUDA C++ skills, including streams and asynchronous execution
Hands-on experience profiling to diagnose compute-bound vs memory-bound bottlenecks
Experience with kernel fusion, memory coalescing and avoiding warp divergence
Experience writing quantised and mixed-precision kernels
Solid grasp of parallel algorithm design and numerical precision tradeoffs

Nice To Haves

transformer/attention-style kernels or autoregressive decoding
building high-performance GPU libraries from scratch
HPC or latency-critical performance engineering
multi-GPU or multi-node kernel-level optimisation
comfortable reading PTX/SASS to validate kernel efficiency

Responsibilities

Write and optimise custom CUDA kernels for core transformer inference operations
Profile kernels to identify and eliminate bottlenecks in occupancy, memory throughput, and warp divergence
Apply kernel fusion to reduce memory round-trips and launch overhead across inference pipelines
Optimise memory access patterns and manage the memory hierarchy for maximum bandwidth utilisation
Implement quantisation-aware kernels and mixed-precision arithmetic to reduce latency and memory footprint
Build and tune caching mechanisms for efficient autoregressive decoding
Tune kernel launch configurations for target GPU architectures
Benchmark kernels against existing baselines and drive measurable throughput and latency improvements
Write tests for CUDA code to catch performance and correctness regressions
Maintain internal CUDA libraries and contribute to team coding standards and documentation