ML Engineer, Platform & RAG Systems (London)

scaleai; Greater London; On-site; GBP 90,000 - 130,000; Full time

Job summary

Scale is seeking a Machine Learning Engineer in London to own end-to-end ML modules, from research to production deployment. You will work with knowledge bases, vector stores and RAG pipelines to power agent-based solutions for enterprise use cases.

The role demands strong Python skills, deep understanding of retrieval and semantic search, and ability to collaborate across product, ML and infrastructure teams. 5+ years of production ML experience is expected.

Qualifications

• Requires 5+ years of experience in ML/AI systems in production.
• Strong fundamentals in CS and software engineering with advanced degree.
• Hands-on understanding of retrieval systems, RAG, embeddings, and semantic search.

Responsibilities

• Own large areas of the platform end to end from design to production.
• Develop and maintain knowledge representations (ontologies, knowledge graphs).
• Design and implement RAG pipelines (chunking, embedding, indexing, retrieval, reranking).
• Integrate retrieval with ML components across data sources and vector DBs.
• Develop context retrieval systems balancing recall, latency and cost.
• Create evaluation datasets and metrics for retrieval quality and end-to-end agent performance.
• Build reliable backend services and data pipelines for ML/LLM components.
• Deliver experiments and new capabilities with high quality and customer feedback loops.
• Collaborate with product, ML, and infra teams to shape platform direction.

Skills

Python proficiency

RAG pipelines

Knowledge of embeddings

Vector databases

Production-grade software

Communication skills

Ambiguity tolerance

Cross-functional collaboration

Education

Master’s or PhD in Computer Science / ML / AI

Tools

Vector stores

Knowledge graphs

Job description

Scale is seeking a Machine Learning Engineer in London to own end-to-end ML modules, from research to production deployment. You will work with knowledge bases, vector stores and RAG pipelines to power agent-based solutions for enterprise use cases.

The role demands strong Python skills, deep understanding of retrieval and semantic search, and ability to collaborate across product, ML and infrastructure teams. 5+ years of production ML experience is expected.