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 and strategic angels, and we're building a fully integrated energy company—developing solar, batteries, and other generation projects, building hardware, improving grid infrastructure, trading power in real time, using AI, and installing distributed energy in homes. By selling directly to consumers, we cut out the middleman, lower costs, and pass savings on to customers. As data centres become a major source of electricity demand, Fuse is expanding into high-performance compute infrastructure at the intersection of energy and AI.
About the role
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
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
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.