Machine Learning Engineer, Platform

integrations between retrieval and ML components and diverse 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 to measure retrieval quality, context relevance, and end to end agent performance.Build reliable backend services and data pipelines that support ML and LLM components in production.Deliver experiments and new capabilities quickly, maintaining high quality and tight feedback loops with customers.Collaborate across product, ML, and infrastructure teams to shape the direction of the platform.Ideally you’d have:5+ years of experience building and deploying machine learning or AI systems for real-world, production use cases.Strong engineering fundamentals, supported by a Master’s or PhD degree in Computer Science, Machine Learning, AI, or equivalent practical experience..A deep, hands-on understanding of retrieval systems, RAG, embeddings

vector indexing, and knowledge representation.Experience with knowledge representation, semantic search, or agentic systems.Proven proficiency in Python, including writing production-quality, testable, and maintainable code.Experience scaling or shipping products at high-growth startups.The ability to operate in ambiguous problem spaces, balancing research-driven approaches with pragmatic product constraints.Strong communication skills and comfort working in customer-facing or cross-functional environments.**PLEASE NOTE:Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants.About Us:*At Scale, our mission is to develop 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, and help enterprises and governments build, deploy, and oversee AI applications that