3 months ago
London, United KingdomSenior
Responsibilities
- Own major platform areas and drive components from design through production deployment.
- Build knowledge representation systems, including ontologies and knowledge graphs, for structured reasoning over enterprise data.
- Design and implement RAG pipelines covering chunking, embedding, indexing, retrieval, and reranking.
- Integrate retrieval and ML components with 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 for retrieval quality, context relevance, and end-to-end agent performance.
- Build reliable backend services and data pipelines supporting ML and LLM components in production.
- Deliver experiments and capabilities quickly while maintaining quality and customer feedback loops.
- Collaborate with product, ML, and infrastructure teams to shape the platform.
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, vector indexing, and knowledge representation.
- Experience with knowledge representation, semantic search, or agentic systems.
- Proficiency in Python and production-quality, testable, maintainable software development.
- Experience scaling or shipping products at high-growth startups.
- Ability to balance research-driven approaches with pragmatic product constraints.
- Strong communication skills and comfort working in customer-facing or cross-functional environments.
Tech Stack
Categories
About Scale AI
Scale AI builds data annotation services and AI development tools for enterprises and government agencies, sold as a platform and managed services. Its products include the Scale Generative AI Platform for building and evaluating agents and the Data Engine for collecting, curating, and labeling training data, including RLHF and model evaluation. Founded in 2016 and headquartered in San Francisco, the company is privately held and works across domains from computer vision to LLM applications.