Senior ML/Data Engineer

About this role

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 MACHINE LEARNING

Catapult Sports is the global leader in athlete performance technology, trusted by elite clubs and national programs across every major sport on every continent. Our hardware and software platforms are on the training ground and in the arena with the best teams in the world — and we have been there, continuously, for over two decades.

We are now 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 — a system that connects the full depth of performance data and surfaces the right insight at the right moment, with the confidence to act on it.

This role is the data foundation of that platform. You will own the infrastructure every AI agent reasons over: the data architecture, the feature store, the sport knowledge graph, and the evaluation framework that makes every recommendation trustworthy before it reaches a practitioner. This is not a support role or a pipeline maintenance job. It is the highest-leverage engineering position in Phase 1 of a platform that will define what performance intelligence means in professional sport.

If you have built data infrastructure at scale — time-series, feature serving, graph — and you care deeply about whether the systems you build are actually trustworthy, not just functional, this is the role that will define your next chapter.

WHAT YOU’LL DO

• You will own the data infrastructure that every AI agent on the platform reasons over. That means designing and building the systems that ingest, store, and serve athlete performance data at scale — from real-time sensor streams to longitudinal historical records — and ensuring every layer is governed, isolated per client, and trustworthy at the moment of a high-stakes decision.

• You will build the feature store that makes derived metrics available to agents in milliseconds, design the sport knowledge graph that encodes relationships between athletes, loads, injuries, and outcomes, and create the evaluation framework that validates every agent’s recommendations against the full distribution of real-world cases before any output reaches a practitioner.

• You will work directly with domain scientists and AI engineers, translating deep sport science requirements into durable, production-grade infrastructure that compounds in value with every season of data added to the platform.

WHAT YOU’LL NEED

NON-NEGOTIABLE

• 5+ years of production data engineering at scale, time-series databases, data lakes, feature stores in a real-time or near-real-time environment

• Experience building probabilistic evaluation frameworks or model calibration infrastructure, you understand the difference between a model that works and a model that is trustworthy

• Strong Python, Golang and SQL; experience with streaming ingestion (not batch-only) for live sensor or IoT data

• Graph database experience: you have designed schemas for complex relationship networks, not just queried existing ones

• Production experience with tenant-level data isolation at the infrastructure level, not just access control

• Comfort working directly with domain scientists and AI engineers; you translate data requirements into durable infrastructure, not just pipelines

STRONGLY PREFERRED

• Experience with causal inference or counterfactual modeling over graph structures

• Background in sports technology, wearable sensor data, or biomechanics data, you understand what makes athlete time-series data structurally different from standard telemetry

• Experience building knowledge graphs with custom ontologies for a specific domain

• Familiarity with LLM evaluation frameworks and their limitations in probabilistic sport science contexts

• Experience working with AWS (ECS, EC2, Lambda, SNS, SQS, etc), GraphQL, REST, gRPC, Postgres, Mongo

THE STACK

No vendor names are locked, the capabilities are what matter, and the right engineer knows how to choose. The platform requires: real-time streaming ingestion; time-series optimised storage at lake scale; millisecond feature serving; graph database with custom sport ontology; causal and simulation modeling layer; full provenance and audit logging; per-club isolation at the infrastructure level.

WHY CATAPULT?

Catapult has spent twenty years collecting ground-truth athlete data from hardware on the body and on the field, across 40+ sports and 100+ countries. That depth of longitudinal, proprietary data is impressive. What we are building now is the intelligence layer that makes that data into something a coach can act on in the moment a decision is required.

The data engineer who builds this foundation will have built the infrastructure layer underneath a platform that compounds in value with every decision made on it. The moat is not just what we know. It is how trustworthy that knowledge is — and that…