Machine Learning Engineer, Platform

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.
• Strong engineering fundamentals, supported by a Master’s or PhD degree in Computer Science, Machine Learning, AI, or equivalent practical experience..
• A deep, hands-on understanding of retrieval systems, RAG, embeddings, vector indexing, and knowledge representation.
• Experience with knowledge representation, semantic search, or agentic systems.
• Proven proficiency in Python, including writing production-quality, testable, and maintainable code.
• Experience scaling or shipping products at high-growth startups.
• The ability to operate in ambiguous problem spaces, balancing research-driven approaches with pragmatic product constraints.
• Strong communication skills and comfort working in customer-facing or cross-functional environments.