Machine Learning Engineer (AI)

TL;DR

Machine Learning Engineer (AI): Building and optimizing retrieval and knowledge representation systems for a Generative AI platform with an accent on RAG pipelines, vector indexing, and knowledge graphs. Focus on designing end-to-end ML components, scaling production-grade AI services, and ensuring high-quality retrieval performance for enterprise customers.

Location: London, UK

Company

Scale AI provides high-quality data and full-stack technologies to power the world's leading AI models and enterprise applications.

What you will do

• Own ML components end-to-end from research and prototyping to production deployment.
• Design and implement RAG pipelines including chunking, embedding, indexing, and reranking.
• Develop knowledge representation systems like ontologies and knowledge graphs for structured reasoning.
• Build and maintain integrations between retrieval systems and diverse enterprise data sources.
• Develop context retrieval systems balancing recall, precision, latency, and cost.
• Build evaluation frameworks and metrics to measure retrieval quality and agent performance.

Requirements

• 5+ years of experience building and deploying production-grade machine learning or AI systems.
• Strong engineering fundamentals with a Master’s or PhD in Computer Science, AI, or equivalent experience.
• Deep hands-on understanding of retrieval systems, RAG, embeddings, and vector indexing.
• Proven proficiency in Python with experience writing production-quality, testable code.
• Experience scaling or shipping products at high-growth startups.
• Ability to operate in ambiguous problem spaces and balance research with product constraints.

Culture & Benefits

• Inclusive and equal opportunity workplace committed to diversity.
• Opportunity to work on cutting-edge Generative AI platforms used by industry leaders.
• Collaborative environment working across product, ML, and infrastructure teams.
• Commitment to providing reasonable accommodations for applicants with disabilities.