Founding GPU Engineer

Fuse Energy

About The Position

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.

Requirements

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.

Nice To Haves

Experience with Triton, cuDNN, cuBLAS, or custom ML inference/training frameworks.
Exposure to data center power/thermal management or demand-response systems.
Background in HPC, quantitative finance, or large-scale distributed systems.
Familiarity with Kubernetes/Slurm for GPU cluster orchestration.
Interest or experience in energy markets, grid systems, or sustainability-focused compute.

Responsibilities

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.