Machine Learning Engineer, Platform

Responsibilities

• Own large areas of the platform end-to-end, driving components from design through to production deployment.
• Design and implement RAG pipelines, including chunking, embedding, indexing, retrieval, and reranking.
• Work on knowledge representation systems, including ontologies and knowledge graphs, to support structured reasoning.
• Build and maintain integrations between retrieval and ML components and diverse enterprise data sources and vector databases.
• Develop context retrieval systems that balance recall, precision, latency, and cost.
• Build evaluation frameworks, datasets, and metrics to measure retrieval quality and agent performance.
• Build reliable backend services and data pipelines that support ML and LLM components in production.

Requirements

• 5+ years of experience building and deploying machine learning or AI systems for real-world production use cases.
• Master’s or PhD in Computer Science, Machine Learning, AI, or equivalent practical experience.
• Deep, hands-on understanding of retrieval systems, RAG, embeddings, and vector indexing.
• Proven proficiency in Python, including writing production-quality, testable, and maintainable code.
• Experience with knowledge representation, semantic search, or agentic systems.

Preferred Qualifications

• Experience scaling or shipping products at high-growth startups.
• Ability to operate in ambiguous problem spaces, balancing research with pragmatic product constraints.
• Strong communication skills and comfort working in cross-functional environments.

About the Company

Scale is developing reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, helping enterprises and governments build, deploy, and oversee AI applications that deliver real impact.