AI Long-Context Chunking Research Engineer

About the role

Responsibilities

• Design and implement novel chunking approaches for long-document retrieval with empirical evaluation
• Build multi-hop retrieval strategies for queries requiring information synthesis across document sections
• Develop information-theoretic chunking evaluation measuring what each strategy preserves versus loses
• Implement context window utilization analysis identifying the effective retrieval threshold per model family
• Create production retrieval benchmarks comparing long-context versus chunked retrieval strategies
• Publish research findings in LLM systems or information retrieval venues

Requirements

• 6+ years in ML research with retrieval systems, LLM applications, or NLP engineering
• Deep understanding of attention mechanisms, context window behavior, and RAG architectures
• Strong Python and PyTorch skills with information retrieval evaluation methodology
• Research track record with publications or significant contributions in LLM systems or IR
• Ability to design rigorous experiments that distinguish genuine improvement from benchmark artifacts

Benefits

• Fully remote with flexible schedule
• Top-of-market compensation for LLM research expertise
• Research conference and publication budget
• Comprehensive health benefits
• Equity participation

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