Staff Scientist Graph And Llm Machine Learning

SignalGraph, Neo4j, and LLM evaluation shape this Staff Data Scientist role at Jobber, connecting business metrics with trusted machine learning.You will continue building SignalGraph while designing retrieval-augmented generation systems on top of its graph. The role requires ownership of retrieval quality, context management, and evaluation infrastructure. You will also apply graph learning, neural architectures, ranking, and production LLM methods to Jobber’s SaaS, scheduling, payments, sales, and support data.Key Responsibilities:
Evolve SignalGraph contracts, catalogs, diagnostics, and refresh pipelinesDesign RAG systems over graph dataOwn retrieval quality, context management, and evaluation harnessesApply graph and deep learning methods in productionTranslate AI and ML developments into shipped capabilityRequirements:
Graph theory, graph neural networks, knowledge graphs, or Neo4j experienceHands-on production experience applying LLMs and RAGDepth in transformers, neural architectures, ranking, and representation learningExperience designing custom or asymmetric loss functionsStrong communication and influence without authorityCombine graph expertise, LLM evaluation, and applied machine learning to advance trusted decision systems at Jobber.
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