Machine Learning Engineer, Platform

About the role

Join the Scale Generative AI Platform (Scale GP) team to build core retrieval and knowledge representation systems. This role involves end-to-end ownership of machine learning components, from initial research to production deployment. You will work on systems that power agents for enterprise clients, focusing on knowledge bases, vector stores, RAG pipelines, and context engines.

Key facts

What you'll do

• Take ownership of significant platform areas, guiding components from initial design to production.
• Develop 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.
• Create and maintain integrations between retrieval/ML components and various enterprise data sources, vector databases, APIs, and services.
• Build context retrieval systems that balance recall, precision, latency, and cost.
• Develop evaluation frameworks, datasets, and metrics to assess retrieval quality, context relevance, and overall agent performance.
• Construct reliable backend services and data pipelines to support ML and LLM components in production.
• Rapidly deliver experiments and new features, maintaining quality and close customer feedback loops.
• Collaborate with product, ML, and infrastructure teams to influence the platform's direction.

Requirements

• 5 or more years of experience developing and deploying machine learning or AI systems for production use.
• Strong engineering fundamentals, demonstrated by a Master's or PhD in Computer Science, Machine Learning, AI, or equivalent practical experience.
• Deep, practical understanding of retrieval systems, RAG, embeddings, vector indexing, and knowledge representation.
• Experience with knowledge representation, semantic search, or agentic systems.
• Proven skill in Python, including writing production-ready, testable, and maintainable code.
• Experience scaling or shipping products at high-growth startups.
• Ability to navigate ambiguous problem spaces, balancing research-driven methods with practical product constraints.
• Strong communication skills and comfort working in customer-facing or cross-functional settings.

Skills & tools

• Python
• Machine Learning
• AI Systems
• Retrieval Systems
• RAG
• Embeddings
• Vector Indexing
• Knowledge Representation
• Semantic Search
• Agentic Systems
• Backend Services
• Data Pipelines

Practical notes

Candidates who have applied for the same role previously will have a 90-day waiting period before being reconsidered.