CUDA Engineer

> Fuse Energy · London, United Kingdom (Remote) · Full-time · Posted 2026-07-19

Workplace: remote

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. 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 CUDA Engineer to write and optimise the low-level GPU code that powers our inference workloads. You'll design custom CUDA kernels, tune performance across memory bandwidth and compute bottlenecks, and squeeze maximum throughput out of every GPU in our fleet, working at the level of SMs, warps, and memory hierarchies.

The Opportunity

Fuse is in active discussions with major AI compute customers who need data center capacity across the markets we operate in, primarily for inference. Demand significantly outpaces what we can currently build, meaning speed to power, reliability, and deployment cost matter more than specific hardware choice. This puts CUDA/GPU performance engineering at the center of how Fuse serves some of the largest compute buyers in the market.

Responsibilities

• Write and optimise custom CUDA kernels for core transformer inference operations.
• Profile kernels to identify and eliminate bottlenecks in occupancy, memory throughput, and warp divergence.