Machine Learning Engineer, Platform London, UK Apply *

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.

We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status. We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at . Please see the United States Department of Labor’s Know Your Rights poster for additional information.

We comply with the United States Department of Labor’s Pay Transparency provision.

• 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.