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.ResponsibilitiesDefine Fuse's inference serving strategy and architecture from first principlesDesign and build the serving stack: request routing, batching, scheduling, and autoscaling for high-throughput, latency-sensitive inference workloadsOwn 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 engineersMake 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 plansAct as a direct technical owner of inference performance and reliabilityWork closely with the CUDA and GPU engineering teams to ensure custom kernels and hardware performance work are integrated cleanly into the serving layerSet the standards, tooling, and benchmarks this function will run on as it growsRequirements4+ years of experience building or operating large-scale inference serving systems, or equivalent strong project/industry experienceDeep, hands-on experience with inference serving frameworks and the techniques used to optimise them (batching, KV-cache management, quantisation, speculative decoding)Strong systems thinking - able to reason about the full path from incoming request to served response across a large clusterComfortable working directly with GPU/CUDA engineers to integrate low-level performance work into a serving systemA track record of making high-stakes architecture calls and owning the outcomeComfort operating without a playbook - this is a founding role shaping a new function around architecture that's still early-stage, not joining an established oneNice to HaveExperience with Triton or custom ML inference/training frameworksExperience with autoscaling or capacity planning for large-scale inference workloadsExposure to multi-tenant serving or SLA-driven infrastructureBackground at a hyperscaler, frontier AI lab, or large-scale distributed inference systemFamiliarity with Kubernetes/Slurm for cluster orchestrationInterest or experience in energy markets, grid systems, or sustainability-focused computeBenefitsCompetitive salary and an equity sign-on bonusBiannual bonus schemeFully expensed tech to match your needsBreakfast and dinner allowance for office based employees