# Founding GPU Engineer

**Company**: Fuse Energy
**Location**: London
**Work arrangement**: remote
**Experience**: senior
**Job type**: full-time
**Category**: Engineering
**Industry**: Energy

**Apply**: https://jobs.workable.com/view/qPYfxrDXGiokCv5JYTF4SB/remote-founding-gpu-engineer-in-london-at-fuse-energy?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_2a67ab53-676

## Description

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.

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.

## 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.

## 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 Have

- 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.

## Benefits

- Competitive salary and an equity sign-on bonus.

- Biannual bonus scheme.

- Fully expensed tech to match your needs.

- Breakfast and dinner allowance for office-based employees.

## Skills

### Required
- CUDA
- C++
- Python
- GPU architecture
- Performance profiling
- Multi-GPU/multi-node scaling
- Memory optimisation
- Parallel algorithm design

### Nice to have
- Triton
- cuDNN
- cuBLAS
- Data center power/thermal management
- Demand-response systems
- HPC
- Quantitative finance
- Large-scale distributed systems
- Kubernetes/Slurm
- Energy markets
- Grid systems
- Sustainability-focused compute

---

Source: [Apply at jobs.workable.com](https://jobs.workable.com/view/qPYfxrDXGiokCv5JYTF4SB/remote-founding-gpu-engineer-in-london-at-fuse-energy?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)