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# AI Inference Engineer

- **Company:** Fuse Limited
- **Location:** UK
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Grid System, Artificial Intelligence, Nvidia CUDA, Uptime, Software Architecture, Large Language Models, Build Management, Kubernetes, Low Latency, SGLang, Slurm, Triton Inference Server, Machine Learning Operations, TensorRT, Model Inference
- **Published:** October 21, 2026
- **Apply:** https://www.totaljobs.com/job/ai-software-developer/fuse-job108175763

## About the Role

* 4+ years 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, not joining an established one
 * Bonus: Triton or custom ML inference/training frameworks; autoscaling or capacity planning for large-scale inference; multi-tenant serving or SLA-driven infrastructure; background at a hyperscaler, frontier AI lab or large-scale distributed inference system; Kubernetes/Slurm; interest in energy markets, grid systems or sustainability-focused compute

## 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 direct technical owner of inference performance and reliability
 * Work closely with the CUDA and GPU engineering teams to integrate custom kernels and hardware performance work cleanly into the serving layer
 * Set the standards, tooling and benchmarks this function will run on as it grows