CUDA Engineer

Description 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, Lower carbon, 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

Requirements4+ 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

Bonus: 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

Benefits

Competitive salary and eligibility for equity

Biannual bonus scheme

Fully expensed tech to match your needs

Private health insurance

Breakfast and dinner allowance for office-based employees

As we hire globally, benefits vary by location.

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Type: full time