AI Inference Engineer

Job not on LinkedIn

🕒 July 20

🌐 United Kingdom, United States, +1 more countries – Remote

🇬🇧 United Kingdom – Remote 🇺🇸 United States – Remote 🇦🇪 United Arab Emirates (UAE) – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 Artificial Intelligence

🇬🇧 UK Skilled Worker Visa Sponsor

This company has sponsored UK Skilled Worker visas in the past.

👻 Ghost score 34%

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Fuse Energy

WebsiteLinkedInAll Job Openings

11 - 50 employees

💼 Consulting

📦 Logistics

⚡ Energy

💰 Venture Round on 2022-09

Consulting • Logistics • Energy

Fuse Energy is an energy company that simplifies the process of switching electricity suppliers and provides real-time billing information. They offer competitive tariffs in the UK and operate solar and wind projects, reinvesting profits into renewable energy initiatives worldwide. Fuse Energy aims to provide customers with transparent billing and easy switching through their app, while promoting sustainability.

📋 Description

• 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.