Agentic AI Engineer)

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 BUILDING AND SHIPPING AGENTIC SYSTEMS

The Agentic AI Engineer is a pivotal role in building the AI layer that compounds everything Catapult has ever measured. The goal is ambitious: to become the indispensable intelligence partner for every coach and athlete in every sport — fielding a bench of AI specialists that can each reason over a different dimension of performance and answer, together, the questions no single human analyst could assemble in real time.

This is the agent build role. You will design and build the specialist agents that form that bench, the workflow engine that encodes domain scientist expertise into validated agent skills at scale, and the decision intelligence layer that transforms agent outputs into calibrated, escalation-aware recommendations a practitioner can trust and act on.

Building an agent is not the hard part. Making an agent trustworthy — calibrated, grounded, escalation-aware, and provenance-traced — is the hard part. That is the standard this role is held to, and the reason it matters.

If you have shipped agentic systems in production — not demos, not prototypes — and you care deeply about what it means for a system to actually earn trust rather than assume it, this is the role where that experience compounds.

What You Will Do

• You will design and build the specialist AI agents that form the core of the platform’s intelligence layer — each one reasoning over a different dimension of athlete performance, from strength and conditioning to recovery, readiness, and beyond.
• You will develop the workflow engine that encodes domain scientist expertise into validated, versioned agent skills at scale, working directly with sport scientists to translate their judgment into calibration signals the system can act on reliably.
• You will architect and build the decision intelligence layer that sits between agent outputs and practitioner delivery — combining confidence weighting, consequence classification, and human escalation logic to ensure every recommendation is defensible before it reaches a coach or performance director.
• You will build toward the platform’s signature product experience: multiple specialist agents working together on a single complex question, synthesizing their findings into one coherent, traceable, calibrated recommendation in seconds.

WHAT YOU’LL NEED

• 5+ years in applied ML or AI engineering, with at least 2 years building production agentic AI systems — not chatbots, not RAG pipelines alone, but systems with memory, tool use, multi-step reasoning, and calibrated outputs
• Deep experience with multi-agent frameworks and orchestration: dependency-aware routing, specialist agent composition, response synthesis across conflicting outputs
• Hands-on experience with confidence calibration and evaluation frameworks for probabilistic systems — you understand Platt scaling, isotonic regression, and ECE, and you have built evaluation harnesses that run against full input distributions
• Production RAG experience with reranking — you know that retrieval quality determines answer quality and have built pipelines that prove it
• Experience fine-tuning or adapting foundation models for specific domains — knowledge injection, not general text
• Strong Python, Golang; experience with LLM observability and drift detection in production

STRONGLY PREFERRED

• Experience building knowledge acquisi…