Machine Learning Engineer, Platform

Scale AI
London
Workplace: OnsiteFull timeFunction: Data Science & Machine LearningExperience: 5+ yearsEducation: mastersSkills: ["Communication","Cross-functional collaboration","Problem-solving","Research-to-production execution"]

Build core retrieval and knowledge representation systems for an enterprise Generative AI platform. Own ML components end to end—from research and prototyping to production deployment—across knowledge bases, vector stores, RAG pipelines, and context engines. Design RAG workflows (chunking, embeddings, indexing, retrieval, reranking), integrate retrieval with ML components and enterprise data sources, and develop evaluation frameworks and metrics to improve retrieval quality and agent performance.

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FursaFursa
Scale AI
Scale AI
2 months ago

Machine Learning Engineer, Platform

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Source: Company careers pageValidated by: Fursa AI
Last checked: 21 hours agoStatus: Live

Job Summary

Build core retrieval and knowledge representation systems for an enterprise Generative AI platform. Own ML components end to end—from research and prototyping to production deployment—across knowledge bases, vector stores, RAG pipelines, and context engines. Design RAG workflows (chunking, embeddings, indexing, retrieval, reranking), integrate retrieval with ML components and enterprise data sources, and develop evaluation frameworks and metrics to improve retrieval quality and agent performance.
Location: London
Workplace: Onsite
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Mid level

Key Responsibilities

  • •Own large areas of the platform end to end, driving components from design through production deployment.
  • •Build knowledge representation systems (ontologies and knowledge graphs) to enable structured reasoning over enterprise data.
  • •Design and implement RAG pipelines including chunking, embedding, indexing, retrieval, and reranking.
  • •Develop context retrieval systems that balance recall, precision, latency, and cost.
  • •Build evaluation frameworks, datasets, and metrics to measure retrieval quality and end-to-end agent performance.

Key Requirements

  • •5+ years building and deploying machine learning or AI systems for real-world, production use cases.
  • •Deep hands-on understanding of retrieval systems, RAG, embeddings, vector indexing, and knowledge representation.
  • •Experience designing knowledge representation approaches such as ontologies, knowledge graphs, semantic search, or agentic systems.
  • •Proficiency in Python for production-quality, testable, maintainable code.
  • •Master’s or PhD degree in Computer Science, Machine Learning, AI, or equivalent practical experience.
Experience:5+ yearsGenerative AIRAGEnterprise AI
Education:Master's in Computer Science, Machine Learning, AI
Skills:CommunicationCross-functional collaborationProblem-solvingResearch-to-production execution
Languages:English
Tech Stack:Machine LearningAIGenerative AIRAGPythonEmbeddingsVector storesVector indexingVector databasesKnowledge representationOntologiesKnowledge graphsSemantic searchAgentic workflowsAPIsLLMsRetrievalRerankingChunking

Company Brief

Scale AI
Provides data labeling, annotation, and infrastructure services to accelerate machine learning and AI development. Supplies high-quality training data, tooling, and APIs for customers in autonomous vehicles, mapping, robotics, and enterprise AI applications.
Industry: AI & Machine Learning
Company Size: Enterprise (1,001+ employees)
Growth: Scaleup
Valuation: Unicorn (USD 1B+)
Funding: Series E+
Headquarters: San Francisco, United States
Founded: 2016
WebsiteLinkedIn