AI Inference Engineer

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 centres 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. We're building the GPU/CUDA performance layer and the inference serving layer at the same time, from scratch - and we're looking for the founding engineer to own the latter.

We're looking for a Founding AI Inference Engineer to define and build how Fuse serves AI inference workloads at scale, reporting directly to the CTO. Where our CUDA and GPU engineering hires own kernel-level and hardware performance, this role owns the layer above it: how models actually get served, scaled, and delivered against committed performance targets.

The OpportunityFuse is seeing significant demand for data centre capacity across the markets we operate in, primarily for inference. Few companies in the world can pair real power delivery with real compute the way Fuse can, which puts inference serving at the heart of how we turn that advantage into the best offering in the market. That's this role.

Responsibilities

• Define Fuse's inference serving strategy and architecture from first principles. • Design and build the serving stack: request routing, batching, scheduling, and autoscaling for high-throughput, latency-sensitive inference workloads. • Own model-level optimisation strategy for serving - deciding where and how to apply quantisation, distillation, speculative decoding, and similar techniques to improve throughput and cost per token, partnering with the CUDA/GPU engineers. • Make the core software architecture calls on serving frameworks and orchestration (e.g. vLLM, TensorRT-LLM, SGLang, Triton Inference Server, or equivalents). • Translate throughput, latency, and uptime commitments into concrete technical specifications and serving capacity plans. • Act as a direct technical owner of inference performance and reliability. • Work closely with the CUDA and GPU engineering teams to ensure custom kernels and hardware performance work are integrated cleanly into the serving layer. • Set the standards, tooling, and benchmarks this function will run on as it grows.