Senior AI/ML Engineer, Knowledge & Retrieval Systems

About usOnebrief is a revolutionary platform for military staff workflows and operational planning. The software is designed to enable smarter, real-time decisions. With unparalleled collaboration features, AI-enhanced tools, and customizable workflows, Onebrief makes staffs superhuman. The expanding roster of customers includes COCOMs and Service Components worldwide.Founded in 2019 by a group of experienced planners, today, Onebrief’s workforce of 120+ spans veterans from all forces and global organizations, and technologists from leading-edge software giants. Onebrief’s growth is exemplary, having raised $53M+ and counting from leading venture investors.Role OverviewWe're seeking a Machine Learning Engineer with a deep understanding of information retrieval, knowledge representation, and edge-deployable ML systems.In this role you will work toward transforming complex, interconnected military operational plans into actionable, queryable knowledge. You'll lead the design and implementation of scalable systems for chunking, indexing, and retrieving rich data from multiple modalities. Your solutions will enable fast, reliable information retrieval to augmented Generative AI systems.Expect to architect hybrid retrieval pipelines that blend semantic search, keyword-based methods, and graph reasoning, optimize embeddings for specialized content, and build resilient systems that power rapid decision-making.We're looking for someone with hands-on experience building real-world retrieval and knowledge-driven systems.What You'll DoDesign and build hybrid retrieval systems that combine semantic, symbolic, and graph-based methodsDevelop pipelines to encode and retrieve operational knowledge using LLMs, vector databases, and custom chunking/indexing strategiesBuild and optimize retrieval-augmented generation (RAG) systems for high-stakes environmentsArchitect knowledge graphs and integrate them into retrieval workflowsCollaborate with ML, product, and domain experts to transform requirements into deployable solutionsKey TechnologiesVector Databases, Hybrid Search PipelinesEmbeddings & Transformer-based modelsKnowledge Graphs (Neo4j, RDF, SPARQL, custom schemas)Python, Distributed Systems, ETL pipelinesDocker, Kubernetes, Edge Computing platformsQualificationsRequired:B.S. in Computer Science, Engineering, or equivalent practical experience2–4 years of experience in applied ML, information retrieval, or knowledge systemsStrong Python programming skillsExperience with semantic search, vector stores, and retrieval system designComfort with ETL workflows and structured, domain-specific datasetsUnderstanding of distributed systems and performance trade-offsFamiliarity with testing and evaluating information retrieval systemsUnderstanding of security considerations in data handling and system designPreferredExperience designing chunking/indexing pipelines for large, domain-specific datasetsExperience designing or deploying knowledge graphs in real-world systemsExperience with offline-capable and edge-deployable ML systemsFamiliarity with containerization and orchestration tools (Docker, Kubernetes)Exposure to geospatial data and reasoning systemsBackground in defense, national security, or other mission-critical domainsUnderstanding of LLM prompt engineering, context window optimization, and RAG techniquesAdvanced degree (M.S. or PhD) in a relevant field is a plusWorking Style:First principles thinking with high ownership mentalityStrong communication and collaboration skillsBias for action - you deliver working systems in imperfect conditionsComfortable working autonomously in a fast-moving startup environment

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