Machine Learning Engineer, Platform
Scale AI•London, UK
Full-time7-15
👁️ 5 views•📝 0 applications•Posted 7/7/2026•Expires 9/29/2026
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Job Description
Machine Learning Engineer, Platform London, UK Scale GP (Scale Generative AI Platform) is an enterprise-grade Generative AI platform that provides APIs for knowledge retrieval, inference, evaluation, and agentic workflows. We are looking for a Machine Learning Engineer to join our team and build the retrieval and knowledge representation systems at the heart of the platform. You will own ML components end to end — from research and prototyping through to production deployment — working across knowledge bases, vector stores, RAG pipelines, and context engines to power agents that deliver real impact for enterprise customers. You will: Own large areas of platform end to end, driving components from design through to production deployment. Work on knowledge representation systems, including ontologies and knowledge graphs, to support structured reasoning over enterprise data. Design and implement RAG pipelines, including chunking, embedding, indexing, retrieval, and reranking. Build and maintain integrations between retrieval and ML components and diverse enterprise data sources, vector databases, APIs, and services. Develop context retrieval systems that balance recall, precision, latency, and cost. Build evaluation frameworks, datasets, and metrics to measure retrieval quality, context relevance, and end to end agent performance. Build reliable backend services and data pipelines that support ML and LLM components in production. Deliver experiments and new capabilities quickly, maintaining high quality and tight feedback loops with customers. Collaborate across product, ML, and infrastructure teams to shape the direction of the platform. Ideally you'd have: 5+ years of experience building and deploying machine learning or AI systems for real-world, production use cases. <l
Required Skills
Machine LearningAI SystemsRetrieval-Augmented Generation (RAG)Knowledge RepresentationOntologiesKnowledge GraphsVector DatabasesEmbeddingsIndexingRetrievalRerankingContext RetrievalEvaluation FrameworksData PipelinesBackend ServicesProduction DeploymentLLM IntegrationEnterprise Data Sources
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