Staff Graph Retrieval Engineer- Startup Backed by Anthropic and Stripe

Job Description

Senior Staff Graph Retrieval Engineer

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About the Opportunity

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Join an elite, venture-backed team building next-generation, AI-powered collaboration tools for the enterprise.

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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.

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Following a recent and substantial round of funding, the company is expanding its world-class engineering team in Colorado and beyond.

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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.

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The Problem We're Solving

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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:

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• Company goals and project hierarchies
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• Cross-team dependencies and relationships
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• Project status, risks, blockers, and opportunities
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• People, roles, and communication patterns
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• Decisions, commitments, and timelines
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An 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:

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• Upstream dependencies (projects Project X depends on)
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• Status of those dependencies (are they at risk?)
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• People involved (are they overcommitted?)
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• Recent decisions that might impact timelines
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• Communication patterns (are teams coordinating effectively?)
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This isn't keyword search. This is graph-structured retrieval over unstructured natural language content where understanding business semantics (dependencies, criticality, risk) is essential.

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What Makes This Role Unique

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Novel Technical Problem

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• Hybrid Structure: Graph structure (dependencies, hierarchies) with unstructured content (natural language descriptions, status updates, Slack messages)
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• Semantic Complexity: Need to understand business concepts like "critical path", "blocking issues", "resource contention", "scope creep"
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• Agentic Retrieval: Classical techniques (graph traversal, embeddings, ranking) PLUS novel approaches (LLM agents crawling their own knowledge graphs)
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• Dynamic Graphs: Knowledge graphs update continuously as we ingest chat messages, docs, emails
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• Query Diversity: From precise ("who owns Project X?") to exploratory ("why might this initiative fail?")
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Full-Stack Ownership

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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:

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• Designs and implements core retrieval algorithms
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• Builds production infrastructure (caching, indexing, APIs)
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• Instruments and optimizes performance
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• Works directly with product to understand use cases
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• Ships fast, iterates based on real user feedback
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Must-Have Experience

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Strong CS Fundamentals:

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• Algorithms and data structures (graph algorithms especially)
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• Complexity analysis and optimization
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• Data systems architecture
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Information Retrieval Expertise:

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• 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.
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• 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.
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• Knowledge Graphs - Implementation Depth: Should have built graph data structures or worked with graph databases (Neo4j, Amazon Neptune, GraphQL engines), not just queried them.
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• Vector Search - Built Not Just Used: Should understand how vector indexes work (HNSW, IVF, product quantization), not just called Pinecone APIs.
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• Hybrid Retrieval: Should have combined multiple retrieval signals (keywords + vectors + graph structure, or dense + sparse retrieval).
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• Query Understanding: Should understand NLP techniques for parsing user intent—entity extraction, query expansion, semantic parsing, or intent classification.
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• 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").
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• Scalability Experience: Should have dealt with large-scale retrieval (millions of documents/nodes, thousands of queries per second).
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• Retrieval for LLMs (RAG): Should understand context window constraints, token budgets, and how to select what to include in LLM context.
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• Real-World Tradeoffs: Should be able to discuss precision vs. recall tradeoffs, latency vs. quality, and when to optimize for each.
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Production Engineering:

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• Built and scaled backend systems (Python, TypeScript, or similar)
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• Experience with databases (PostgreSQL, vector DBs)
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• API design and performance optimization
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• Comfortable with full dev lifecycle (design → build → deploy → monitor)
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Modern ML/LLM Knowledge:

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• Understanding of RAG (Retrieval-Augmented Generation) architectures
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• Familiarity with LLM capabilities and limitations
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• Bonus: experience building with LLM APIs (OpenAI, Anthropic)
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• Bonus: agentic systems or multi-agent orchestration
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Educational Background

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Ideal:

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• MS or PhD in Computer Science (specializing in IR, ML, NLP, or Data Systems)
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• Top undergrad CS program (Stanford, MIT, CMU, Berkeley, etc.) with relevant coursework
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Acceptable:

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• Strong BS in CS with exceptional work experience in search/retrieval
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• Self-taught engineers with deep domain expertise (rare but possible)
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Bonus:

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• Published papers in IR conferences (SIGIR, WSDM, RecSys)
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• Contributions to open-source search/graph projects
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• Side projects demonstrating depth in knowledge graphs or RAG
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Compensation & Benefits:

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• Competitive salary and meaningful equity packages. (Equity is the big story- take time to learn about that story throughout your interviews).
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• Recent offers for comparable roles have ranged from $200,000–$250000 base , plus meaningful signing bonuses and strong equity stakes.
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• Very solid Health benefits (Medical, Dental, and Vision)
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• 401K match
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• Flexible 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)
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• Opportunity to collaborate with world-class technical peers on groundbreaking AI systems.
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