ML/Data Engineer

About the role

Build and run production data infrastructure for athlete performance and sensor data, supporting machine learning and AI systems. The role lasts about a year and works with sport scientists and ML engineers.

What you will do

• Design and operate data pipelines that ingest streaming and time-series data at high volume
• Create storage and data models, including graph schemas, for athlete records and performance metrics
• Provide low-latency foundations so ML models and AI agents can use production features and metrics
• Build evaluation tooling that measures model reliability, calibration and performance in real cases
• Ensure tenant-level data isolation, provenance, lineage, auditability and observability across the platform
• Collaborate with ML engineers and domain experts to turn complex needs into durable production systems

What they look for

• Track record of designing, building and running production data infrastructure at scale
• Strong background with time-series data or databases such as InfluxDB, TimescaleDB, Prometheus or ClickHouse
• Hands-on experience with real-time or streaming ingestion using Kafka, Kinesis, Flink, Spark Streaming or Pulsar
• Solid Python and SQL skills, plus comfort owning ambiguous technical problems

Nice to have

• Professional Go experience
• Causal inference, counterfactual modelling or simulation work
• Wearable sensors, IoT, biomechanics or sports technology telemetry