Senior ML/Data Engineer

Join Catapult Sports, the global leader in athlete performance technology. As a Senior ML/Data Engineer, you will be responsible for building the data foundation of our AI platform, which aims to become the indispensable intelligence partner for every coach and athlete in every sport. You will own the data architecture, feature store, sport knowledge graph, and evaluation framework. This is a high-leverage engineering position that will define the future of performance intelligence in professional sports.

• Ownership of the data architecture, feature store, sport knowledge graph, and evaluation framework for the AI platform.
• Development and execution of strategic workforce plans, coaching senior leaders on organizational design, change management, and talent development.
• Mentorship and leadership of regional P&C team members, fostering a high-performance culture and driving engagement and retention.
• 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
• Comfort working directly with domain scientists and AI engineers; you translate data requirements into durable infrastructure, not just pipelines
• Graph database experience: you have designed schemas for complex relationship networks, not just queried existing ones
• 5+ years of production data engineering at scale, time-series databases, data lakes, feature stores in a real-time or near-real-time environment
• Strong Python, Golang and SQL; experience with streaming ingestion (not batch-only) for live sensor or IoT data
• Experience building probabilistic evaluation frameworks or model calibration infrastructure, you understand the difference between a model that works and a model that is trustworthy
• Production experience with tenant-level data isolation at the infrastructure level, not just access control
• 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
• Research shows that while men apply for jobs when they meet an average of 60% of the criteria, women and other marginalized groups tend only to apply when they check every box. So if you have what it takes, but don't meet every single point in our job ad, please still get in touch! We would love to have a chat and see if you could be a great addition to our team