{"schemaVersion":"jobsearcher.job.v1","id":"7fdffc7cbce07773509ea4c2","url":"https://jobsearcher.com/jobs/7fdffc7cbce07773509ea4c2","canonicalUrl":"https://jobsearcher.com/jobs/7fdffc7cbce07773509ea4c2","title":"Senior Staff Graph Retrieval Engineer","description":"Senior Staff Graph Retrieval EngineerJoin an elite, venture-backed team building next-generation, AI-powered collaboration tools for the enterprise.Technical Integrity has been retained to lead the search for a rapidly growing startup developing AI systems that improve how large organizations think, communicate, and make decisions. Their product uses cutting-edge AI to identify and resolve coordination gaps automatically — helping teams operate more intelligently and efficiently at scale.Following a recent and substantial round of funding, the company is expanding its world-class engineering team in Colorado and beyond.We're hiring a senior staff engineer specializing in knowledge graph retrieval to solve a critical scaling challenge: our AI agents build massive English-language knowledge graphs of enterprise operations, and we need intelligent retrieval systems to extract relevant information from graphs that are 100-10000x larger than any LLM context window. This is a novel information retrieval problem combining classical search/ranking techniques with cutting-edge agentic LLM approaches, applied to highly unstructured natural language knowledge graphs.Our product builds personal AI agents for leaders and managers in large, complex organizations. Each agent constructs a \"world model\" - an English-language knowledge graph capturing:Company goals and project hierarchiesCross-team dependencies and relationshipsProject status, risks, blockers, and opportunitiesPeople, roles, and communication patternsDecisions, commitments, and timelinesAn example of the retrieval problem: When a leader asks \"what could cause Project X to run behind?\", we need to intelligently traverse the knowledge graph to find:Upstream dependencies (projects Project X depends on)Status of those dependencies (are they at risk?)People involved (are they overcommitted?)Recent decisions that might impact timelinesCommunication patterns (are teams coordinating effectively?)This isn't keyword search. This is graph-structured retrieval over unstructured natural language content where understanding business semantics (dependencies, criticality, risk) is essential.Full-stack ownership. This is a small team (11 people) building for massive enterprises. The ideal candidate needs to be able to handle the algorithmic core of this problem plus the surrounding tooling and infra:Designs and implements core retrieval algorithmsBuilds production infrastructure (caching, indexing, APIs)Instruments and optimizes performanceWorks directly with product to understand use casesShips fast, iterates based on real user feedbackMust-have experience:Strong CS Fundamentals:Algorithms and data structures (graph algorithms especially)Complexity analysis and optimizationData systems architectureInformation Retrieval Expertise:Search Ranking & Relevance: Should be able to discuss specific algorithms (TF-IDF, BM25, learning-to-rank models like LambdaMART) and explain when to use each.Evaluation & Metrics: Must understand precision, recall, F1, NDCG, MRR (Mean Reciprocal Rank). Should have run offline evaluations and A/B tests to measure retrieval quality.Knowledge Graphs - Implementation Depth: Should have built graph data structures or worked with graph databases (Neo4j, Amazon Neptune, GraphQL engines), not just queried them.Vector Search - Built Not Just Used: Should understand how vector indexes work (HNSW, IVF, product quantization), not just called Pinecone APIs.Hybrid Retrieval: Should have combined multiple retrieval signals (keywords + vectors + graph structure, or dense + sparse retrieval).Query Understanding: Should understand NLP techniques for parsing user intent—entity extraction, query expansion, semantic parsing, or intent classification.Multi-Hop Reasoning: Bonus if they've built systems that retrieve information across multiple documents or hops (e.g., \"find papers cited by papers that cite this paper\").Scalability Experience: Should have dealt with large-scale retrieval (millions of documents/nodes, thousands of queries per second).Retrieval for LLMs (RAG): Should understand context window constraints, token budgets, and how to select what to include in LLM context.Real-World Tradeoffs: Should be able to discuss precision vs. recall tradeoffs, latency vs. quality, and when to optimize for each.Production Engineering:Built and scaled backend systems (Python, TypeScript, or similar)Experience with databases (PostgreSQL, vector DBs)API design and performance optimizationComfortable with full dev lifecycle (design → build → deploy → monitor)Modern ML/LLM Knowledge:Understanding of RAG (Retrieval-Augmented Generation) architecturesFamiliarity with LLM capabilities and limitationsBonus: experience building with LLM APIs (OpenAI, Anthropic)Bonus: agentic systems or multi-agent orchestrationEducational Background:Ideal:MS or PhD in Computer Science (specializing in IR, ML, NLP, or Data Systems)Top undergrad CS program (Stanford, MIT, CMU, Berkeley, etc.) with relevant courseworkAcceptable:Strong BS in CS with exceptional work experience in search/retrievalSelf-taught engineers with deep domain expertise (rare but possible)Bonus:Published papers in IR conferences (SIGIR, WWW, WSDM, RecSys)Contributions to open-source search/graph projectsSide projects demonstrating depth in knowledge graphs or RAGCompensation & Benefits:Competitive salary and meaningful equity packages. (Equity is the big story- take time to learn about that story throughout your interviews).Recent offers for comparable roles have ranged from $275,000–$325,000 base, plus meaningful signing bonuses and equity stakes.Very solid Health benefits (Medical, Dental, and Vision)401K matchFlexible work arrangements — Ideal situation is someone able to spend a day or two per week in the office in Boulder, Colorado, next is remote US (with a preference for NYC or the Bay Area)Opportunity to collaborate with world-class technical peers on groundbreaking AI systems.To apply, please contact Technical Integrity with your resume and a concise statement of interest.We value transparency, prompt feedback, and a respectful candidate experience throughout.If you're a senior software engineer or principal technologist who thrives on autonomy, deep technical challenges, and building at the frontier of AI-assisted engineering, we'd love to hear from you.","company":"Technical Integrity","rawCompany":"technical integrity","city":"Boulder","state":"CO","isRemote":false,"isActive":false,"createdAt":"2026-06-26T02:32:23.719Z","occupations":[{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541990","title":"All Other Professional, Scientific, and Technical Services","slug":"all-other-professional-scientific-and-technical-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Senior Staff Graph Retrieval Engineer","description":"Senior Staff Graph Retrieval EngineerJoin an elite, venture-backed team building next-generation, AI-powered collaboration tools for the enterprise.Technical Integrity has been retained to lead the search for a rapidly growing startup developing AI systems that improve how large organizations think, communicate, and make decisions. Their product uses cutting-edge AI to identify and resolve coordination gaps automatically — helping teams operate more intelligently and efficiently at scale.Following a recent and substantial round of funding, the company is expanding its world-class engineering team in Colorado and beyond.We're hiring a senior staff engineer specializing in knowledge graph retrieval to solve a critical scaling challenge: our AI agents build massive English-language knowledge graphs of enterprise operations, and we need intelligent retrieval systems to extract relevant information from graphs that are 100-10000x larger than any LLM context window. This is a novel information retrieval problem combining classical search/ranking techniques with cutting-edge agentic LLM approaches, applied to highly unstructured natural language knowledge graphs.Our product builds personal AI agents for leaders and managers in large, complex organizations. Each agent constructs a \"world model\" - an English-language knowledge graph capturing:Company goals and project hierarchiesCross-team dependencies and relationshipsProject status, risks, blockers, and opportunitiesPeople, roles, and communication patternsDecisions, commitments, and timelinesAn example of the retrieval problem: When a leader asks \"what could cause Project X to run behind?\", we need to intelligently traverse the knowledge graph to find:Upstream dependencies (projects Project X depends on)Status of those dependencies (are they at risk?)People involved (are they overcommitted?)Recent decisions that might impact timelinesCommunication patterns (are teams coordinating effectively?)This isn't keyword search. This is graph-structured retrieval over unstructured natural language content where understanding business semantics (dependencies, criticality, risk) is essential.Full-stack ownership. This is a small team (11 people) building for massive enterprises. The ideal candidate needs to be able to handle the algorithmic core of this problem plus the surrounding tooling and infra:Designs and implements core retrieval algorithmsBuilds production infrastructure (caching, indexing, APIs)Instruments and optimizes performanceWorks directly with product to understand use casesShips fast, iterates based on real user feedbackMust-have experience:Strong CS Fundamentals:Algorithms and data structures (graph algorithms especially)Complexity analysis and optimizationData systems architectureInformation Retrieval Expertise:Search Ranking & Relevance: Should be able to discuss specific algorithms (TF-IDF, BM25, learning-to-rank models like LambdaMART) and explain when to use each.Evaluation & Metrics: Must understand precision, recall, F1, NDCG, MRR (Mean Reciprocal Rank). Should have run offline evaluations and A/B tests to measure retrieval quality.Knowledge Graphs - Implementation Depth: Should have built graph data structures or worked with graph databases (Neo4j, Amazon Neptune, GraphQL engines), not just queried them.Vector Search - Built Not Just Used: Should understand how vector indexes work (HNSW, IVF, product quantization), not just called Pinecone APIs.Hybrid Retrieval: Should have combined multiple retrieval signals (keywords + vectors + graph structure, or dense + sparse retrieval).Query Understanding: Should understand NLP techniques for parsing user intent—entity extraction, query expansion, semantic parsing, or intent classification.Multi-Hop Reasoning: Bonus if they've built systems that retrieve information across multiple documents or hops (e.g., \"find papers cited by papers that cite this paper\").Scalability Experience: Should have dealt with large-scale retrieval (millions of documents/nodes, thousands of queries per second).Retrieval for LLMs (RAG): Should understand context window constraints, token budgets, and how to select what to include in LLM context.Real-World Tradeoffs: Should be able to discuss precision vs. recall tradeoffs, latency vs. quality, and when to optimize for each.Production Engineering:Built and scaled backend systems (Python, TypeScript, or similar)Experience with databases (PostgreSQL, vector DBs)API design and performance optimizationComfortable with full dev lifecycle (design → build → deploy → monitor)Modern ML/LLM Knowledge:Understanding of RAG (Retrieval-Augmented Generation) architecturesFamiliarity with LLM capabilities and limitationsBonus: experience building with LLM APIs (OpenAI, Anthropic)Bonus: agentic systems or multi-agent orchestrationEducational Background:Ideal:MS or PhD in Computer Science (specializing in IR, ML, NLP, or Data Systems)Top undergrad CS program (Stanford, MIT, CMU, Berkeley, etc.) with relevant courseworkAcceptable:Strong BS in CS with exceptional work experience in search/retrievalSelf-taught engineers with deep domain expertise (rare but possible)Bonus:Published papers in IR conferences (SIGIR, WWW, WSDM, RecSys)Contributions to open-source search/graph projectsSide projects demonstrating depth in knowledge graphs or RAGCompensation & Benefits:Competitive salary and meaningful equity packages. (Equity is the big story- take time to learn about that story throughout your interviews).Recent offers for comparable roles have ranged from $275,000–$325,000 base, plus meaningful signing bonuses and equity stakes.Very solid Health benefits (Medical, Dental, and Vision)401K matchFlexible work arrangements — Ideal situation is someone able to spend a day or two per week in the office in Boulder, Colorado, next is remote US (with a preference for NYC or the Bay Area)Opportunity to collaborate with world-class technical peers on groundbreaking AI systems.To apply, please contact Technical Integrity with your resume and a concise statement of interest.We value transparency, prompt feedback, and a respectful candidate experience throughout.If you're a senior software engineer or principal technologist who thrives on autonomy, deep technical challenges, and building at the frontier of AI-assisted engineering, we'd love to hear from you.","datePosted":"2026-06-26T02:32:23.719Z","dateModified":"2026-06-26T02:32:23.719Z","hiringOrganization":{"@type":"Organization","name":"Technical Integrity","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Boulder","addressRegion":"CO","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"7fdffc7cbce07773509ea4c2"},"url":"https://jobsearcher.com/jobs/7fdffc7cbce07773509ea4c2"}}