Founding GPU Engineer - Equity & Biannual Bonus

Fuse Energy; Greater London; On-site; GBP 90,000 - 130,000; Full time

Benefits offered by this job

Equity sign-on bonus

Biannual bonus

Fully expensed tech

Breakfast and dinner allowance

Job summary

Fuse Energy is seeking a seasoned CUDA performance engineer to design and optimise kernels for high-throughput workloads in a high-performance compute environment.

You will profile GPU bottlenecks, build tooling to align power draw with energy pricing, and optimise multi-GPU scaling across NCCL/MPI. Collaboration with ML and systems teams is essential to improve training and inference pipelines.

Qualifications

• 4+ years of experience writing production CUDA code or equivalent.
• Deep understanding of GPU architecture (SMs, warps, memory hierarchy).
• Proficiency in C++ and CUDA; Python for tooling/automation.
• Experience with performance profiling tools (Nsight Systems/Compute).
• Familiarity with multi-GPU/multi-node scaling (NCCL, MPI, RDMA/InfiniBand).
• Strong memory optimization, kernel fusion, and parallel algorithm design.
• Comfortable from kernel to system-level infra.

Responsibilities

• Design, implement, and optimise CUDA kernels for high-throughput workloads.
• Profile GPU performance across compute, memory, and interconnect bottlenecks.
• Build tooling to correlate GPU power draw with energy pricing and grid signals.
• Optimise multi-GPU/multi-node scaling using NCCL, MPI, or similar libraries.
• Collaborate with data center teams on power capping and DVFS strategies.
• Integrate custom kernels into ML training/inference pipelines.
• Benchmark against CPU/GPU baselines and drive improvements.
• Contribute to internal libraries, docs, and best practices for GPU performance.

Skills

CUDA programming

C++

Python tooling

GPU architecture knowledge

Performance profiling

Multi-GPU scaling

Memory optimization

Energy-aware compute

Tools

Nsight Systems

NCCL

MPI

RDMA/InfiniBand

Job description

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 including Balderton, Lakestar, Accel, Creandum, Lowercarbon, Ribbit, 20VC, Hummingbird and Collaborative Fund, alongside strategic angels including Nico Rosberg and GPs behind Meta, Revolut, Spotify and Uber.

We're building a fully integrated energy company: developing our own solar, batteries and other generation projects, building our own hardware, improving and developing grid infrastructure, trading power in real time, using AI across the business, and installing distributed energy in homes. By selling directly to consumers we cut out the middleman, lower costs and pass the savings on to our customers.

As data centres become one of the largest and fastest-growing sources of electricity demand, Fuse is expanding into high-performance compute infrastructure 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 centre systems: low-level performance engineering for large-scale compute clusters, tying GPU workload behaviour to energy availability and grid demand. This puts CUDA/GPU performance engineering at the centre of how Fuse scales its compute infrastructure.

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

• 4+ years 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
• Bonus: Triton, cuDNN, cuBLAS or custom ML inference/training frameworks; data centre power/thermal management or demand-response systems; HPC, quantitative finance or large-scale distributed systems; Kubernetes/Slurm for GPU cluster orchestration; interest in energy markets, grid systems or sustainability-focused compute

• 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.