About this role
Salary: £68,000 - 108,000 per year
Requirements: Experience shipping machine learning systems used by real usersExperience owning machine learning systems end-to-end in productionExperience working with modern LLMs beyond simple API integrationPractical experience debugging model behaviour and analysing failuresExperience optimising latency, throughput, and costFamiliarity with monitoring, observability, and evaluation frameworksExperience scaling machine learning systems in production environmentsFamiliarity with Python, PyTorch or modern machine learning frameworks, LLM platforms, GPU-based training and inference, Docker, Kubernetes, and AWS, Azure, or GCP Responsibilities: Design and operate systems that enable machine learning models to run reliably in productionBuild and maintain end-to-end pipelines from training through inferenceSupport continuous evaluation and iterative improvement of machine learning systemsDevelop training, inference, and evaluation pipelines for LLM-based systems and agent-style workflowsDebug model behaviour using real-world signalsOptimise system performance across latency, cost, and reliabilityMaintain production monitoring, logging, and system stability Technologies: AIAPIAWSAzureDockerSupportKubernetesLLMMachine LearningPyTorchPythonCloudBackendGCP More:
We are working with a well-established, technology-led business building a new AI product focused on real-world tasks, workflows, and decision-making. Our product supports long-running AI workflows, persistent context across interactions, multi-step reasoning and task execution, and integration with external tools and systems. This role is at the core of the machine learning layer powering the product, where we turn model capabilities into reliable, production-grade systems. We are a small, high-calibre engineering team with a strong emphasis on ownership, delivery, fast iteration, and real user feedback. We are fully remote-first, with the role based anywhere in the United Kingdom, and offer permanent employment with flexible compensation and equity. We value pragmatic decision-making and comfort working in evolving systems; our emphasis is on how systems are built and operated rather than on specific tools.
last updated 40 week of 2026