About the Role

Katalyze AI is looking for a Senior ML Scientist to design, build, and deploy the core intelligence layer of our platform. You'll work across two interconnected domains:  RAG and knowledge retrieval  (making our platform reason accurately over large scientific document corpora) and  agentic AI systems  (building the multi-step reasoning pipelines that autonomously complete complex tasks for our biopharma and manufacturing customers).

This is a research-forward role with direct impact on production. You'll go from reading a paper to shipping a production system; sometimes in the same week.

• Design and build advanced RAG pipelines for scientific knowledge retrieval: chunking strategies, embedding model selection, hybrid search, re-ranking, and rigorous retrieval evaluation
• Develop and maintain Knowledge Graph architectures (Neo4j, ontologies, semantic structures) that capture domain relationships and give agents deep understanding of biopharma and manufacturing workflows
• Architect agentic workflows using LangChain/LangGraph or custom orchestration: designing autonomous, multi-step reasoning pipelines for complex enterprise tasks
• Build the "skills layer" that allows agents to execute domain-specific tasks reliably, with proper validation, auditability, and error handling for high-stakes regulated environments
• Advance entity extraction and knowledge representation: building systems that turn unstructured scientific documents into structured, queryable domain knowledge
• Design and run rigorous evaluation frameworks to benchmark agent reliability, RAG accuracy, and model consistency — define what "good enough to ship" looks like
• Stay current with ML research (NeurIPS, ICML, ICLR, ACL) and identify applicable advances; translate them from paper to production
• Collaborate with the Data Science, Engineering, and Product teams to integrate ML components into customer-facing features

What We're Looking For

• 4+ years of applied ML research or engineering experience, with production deployments under your belt
• Deep RAG expertise: chunking, embedding models, vector databases (Pinecone, Weaviate, pgvector), hybrid retrieval, context window optimization, and evaluation methodology
• Hands-on experience with Knowledge Graph construction (Neo4j, RDF/OWL, property graphs) and graph-based reasoning

Tech Stack:

• Agent Frameworks: LangChain, LangGraph, custom orchestration
• LLM Providers: Anthropic Claude, OpenAI, AWS Bedrock
• Knowledge Systems: Neo4j, custom ontologies, semantic search, pgvector / Pinecone
• ML & Research: Python, PyTorch, Hugging Face, scikit-learn, pandas
• Infrastructure: AWS, Docker