Principal AI Engineer

Responsibilities

• Design and build the knowledge graph architecture in Neo4j. A structured representation of how sports data, practitioner behaviour, and domain concepts relate to each other. Temporal facts, community detection, confidence scoring.
• Build the context synthesis pipeline: the system that assembles the right subgraph into the right context for the right query. Map-reduce over graph communities. The quality of the agent's output is limited by the quality of the context you assemble.
• Build the intelligence service in Go that turns structured knowledge into practitioner-facing answers. Not text summaries. Rich, evidence-backed responses with provenance, a practitioner can click through.
• Build the integration layer that connects Catapult's product data (wearables, gym, video, positioning) into the graph. Multiple sources, different frequencies, different schemas, one connected knowledge layer.
• Build internal validation tools so domain experts can see, challenge, and correct the system's output. The correction IS the product's most valuable training signal.
• Set the technical standard for a team that uses AI coding tools as the primary development environment, not a supplement. Multiple parallel sessions. Written communication over meetings. Async-first.
• Sit on the architecture committee. You connect this team to the broader engineering organisation's technical governance.

Requirements

• Required (you won't tick every box, we're looking for the person, not the checklist):
• You have built systems that use LLMs for reasoning over structured data. At work, on a side project, or at 2am because you couldn't stop. Not chatbots. Not RAG over documents. Systems where an LLM reasons

Benefits

Additional Information

Catapult is building the future of sports performance technology, with a mission to Unleash the Potential of every athlete and team on earth. We don't just work in the sporting industry; we are actively changing it. Since 2006, our solutions have been leading the way in sports performance software, science, and data, in a world where 1% can literally mean the difference between winning and losing. We work with over 5,000+ teams around the world, empowering coaches, managers and trainers in premier teams in the NFL, NBA, NHL, MLS, EPL, AFL, NRL, NCAA and more. We provide the information they need to optimize athletes' health, game-day readiness, and performance, as well as in-game tactics. Catapult is a sports technology company that empowers professional teams to make data-driven decisions. We deliver health, performance, video, and AI insights from the locker room to competitive environments, ensuring every decision is an opportunity to gain an advantage, sharpen performance, and build lasting success. WE WANT PEOPLE WHO ARE PASSIONATE ABOUT USING ARTIFICIAL INTELLIGENCE TO BUILD AMAZING PRODUCTS We are looking for a Principal AI Engineer who thinks in graphs, builds in Go, and believes the value of an AI system lives in the knowledge structure underneath it, not the language model on top. Based in Melbourne, you will own the core intelligence architecture for Catapult's next-generation platform: connecting the measurements we're known for into a system that reasons about sport. This is a small team with executive sponsorship, building at the intersection of knowledge graphs, LLM reasoning, and elite sports data. If you care more about getting the architecture right than following a process, we should talk. We are building a system that reasons about sport. Not a chatbot on top of dashboards. Not a retrieval system that finds the right spreadsheet. A sensemaking system that connects all the measurements Catapult is known for into a knowledge graph, and lets anyone from the GM to the sideline coach ask questions in the language of their sport and get evidence-backed answers. The major cloud and enterprise AI companies have validated this architecture pattern in production. Nobody has built it for sport. We are. You will own the core intelligence architecture: the Neo4j knowledge graph, the context synthesis pipeline (map-reduce over graph communities), the agent orchestration, and the evaluation framework that keeps it honest. You are the most senior technical person on a small team. There is no technical hierarchy above you. The Head of AI sets direction, not architecture. You make the technical calls. The stack: Go for the intelligence service, Neo4j for the knowledge graph, AWS Bedrock for model hosting, PromptFoo for eval, Python for data pipelines, React for practitioner tools, Rust for performance-critical paths. You don't need to know all of these on day one. You need to be able to learn any of them in a week.

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