The ad says“Proficiency in C++ and CUDA; experience with Python for tooling/orchestration”
The ad says“Experience with Triton, cuDNN, cuBLAS, or custom ML inference/training frameworks”
The ad says“Proficiency in C++ and CUDA; experience with Python for tooling/orchestration”
The ad says“Experience with Triton, cuDNN, cuBLAS, or custom ML inference/training frameworks”
The ad says“Deep understanding of GPU architecture (SMs, warps, memory hierarchy, occupancy)”
The ad says“Strong grasp of memory optimisation, kernel fusion, and parallel algorithm design”
The ad says“Familiarity with Kubernetes/Slurm for GPU cluster orchestration”
The ad says“Strong grasp of memory optimisation, kernel fusion, and parallel algorithm design”
The ad says“Familiarity with multi-GPU/multi-node scaling (NCCL, MPI, RDMA/InfiniBand)”
The ad says“Familiarity with multi-GPU/multi-node scaling (NCCL, MPI, RDMA/InfiniBand)”
The ad says“Experience with performance profiling tools (Nsight Systems/Compute)”
The ad says“Experience with performance profiling tools (Nsight Systems/Compute)”
The ad says“Strong grasp of memory optimisation, kernel fusion, and parallel algorithm design”
The ad says“Proficiency in C++ and CUDA; experience with Python for tooling/orchestration”
The ad says“Familiarity with multi-GPU/multi-node scaling (NCCL, MPI, RDMA/InfiniBand)”
The ad says“Familiarity with Kubernetes/Slurm for GPU cluster orchestration”
The ad says“Experience with Triton, cuDNN, cuBLAS, or custom ML inference/training frameworks”
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
Preferred experience
Proficiency in C++ and CUDA; experience with Python for tooling/orchestration
Deep understanding of GPU architecture (SMs, warps, memory hierarchy, occupancy)
Experience with performance profiling tools (Nsight Systems/Compute)
Comfortable working across the stack from low-level kernels to system-level infrastructure
Strong grasp of memory optimisation, kernel fusion, and parallel algorithm design
4+ years of experience writing production CUDA code, or equivalent strong project/industry experience
Familiarity with multi-GPU/multi-node scaling (NCCL, MPI, RDMA/InfiniBand)
Experience with Triton, cuDNN, cuBLAS, or custom ML inference/training frameworks
Exposure to data center power/thermal management or demand-response systems
Familiarity with Kubernetes/Slurm for GPU cluster orchestration
Background in HPC, quantitative finance, or large-scale distributed systems
Interest or experience in energy markets, grid systems, or sustainability-focused compute
