Founding GPU Engineer, Fuse Energy

• 4+ years of experience writing production CUDA code, or equivalent strong project/industry experience
• Deep understanding of GPU architecture (SMs, warps, memory hierarchy, occupancy)
• Proficiency in C++ and CUDA; experience with Python for tooling/orchestration
• Experience with performance profiling tools (Nsight Systems/Compute)
• Familiarity with multi-GPU/multi-node scaling (NCCL, MPI, RDMA/InfiniBand)
• Strong grasp of memory optimisation, kernel fusion, and parallel algorithm design
• Comfortable working across the stack from low-level kernels to system-level infrastructure

Desirable

• Fuse Energy is a forward-thinking renewable energy startup on a mission to deliver a terawatt of renewable energy - fast. We're combining first-principles thinking with cutting-edge technology to build a radically better energy system
• We raised $210M from top-tier investors including Multicoin, Balderton, Lakestar, Accel, Creandum, Lowercarbon, Ribbit, Box Group and strategic angels like Nico Rosberg, the Co-Founder of Solana and GPs behind Meta, Revolut, Spotify, Uber and more
• As data centers become one of the largest and fastest-growing sources of electricity demand, Fuse is expanding into high-performance compute infrastructure that sits 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 center systems
• You'll work on low-level performance engineering for large-scale compute clusters, helping Fuse build the software layer that ties GPU workload behaviour to energy availability and grid demand
• Demand for high-performance compute capacity across the markets we operate in significantly outpaces what we can currently build, meaning speed to power and reliability are critical to how we scale. This puts CUDA/GPU performance engineering at the center of how Fuse scales its compute infrastructure
• 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