CUDA 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. 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.

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