# AI Inference Engineer **Company**: Fuse Energy **Work arrangement**: remote **Experience**: senior **Job type**: full-time **Category**: Engineering **Industry**: Technology **Apply**: https://jobs.workable.com/view/d8L9Rw7VMr8fCNDwZqPaQ6/remote-ai-inference-engineer-in-united-states-at-fuse-energy?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply **Canonical**: https://yubhub.co/jobs/job_0e33011f-86a ## 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 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. ## 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. ## Requirements - 4+ years of experience building or operating large-scale inference serving systems, or equivalent strong project/industry experience. - Deep, 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 cluster. - Comfortable working directly with GPU/CUDA engineers to integrate low-level performance work into a serving system. - A track record of making high-stakes architecture calls and owning the outcome. - Comfort operating without a playbook - this is a founding role shaping a new function around architecture that's still early-stage, not joining an established one. ## Nice to Have - Experience with Triton or custom ML inference/training frameworks. - Experience with autoscaling or capacity planning for large-scale inference workloads. - Exposure to multi-tenant serving or SLA-driven infrastructure. - Background at a hyperscaler, frontier AI lab, or large-scale distributed inference system. - Familiarity with Kubernetes/Slurm for 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 - large-scale inference serving systems - inference serving frameworks - systems thinking - GPU/CUDA engineering - architecture calls ### Nice to have - Triton - custom ML inference/training frameworks - autoscaling - capacity planning - multi-tenant serving - SLA-driven infrastructure - Kubernetes/Slurm --- Source: [Apply at jobs.workable.com](https://jobs.workable.com/view/d8L9Rw7VMr8fCNDwZqPaQ6/remote-ai-inference-engineer-in-united-states-at-fuse-energy?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)