ML/Data Engineer
Core
Design and build production data infrastructure for high-volume athlete performance and sensor data to support machine learning systems and AI agents.
Role type
Senior ML/Data Engineer
Builds
Real-time ingestion systems, time-series data storage, graph data models, and data/ML evaluation infrastructure.
Domain
Sports technology / Biomechanics / IoT
Deliverable
production ML models
Required skills
Python, SQL, Go, Kafka, Flink, Spark Streaming, InfluxDB, TimescaleDB, ClickHouse, AWS (ECS, EC2, Lambda, SNS, SQS), Postgres, MongoDB, GraphQL, gRPC, graph database design, multi-tenant architecture, time-series data handling, streaming data ingestion, causal inference, counterfactual modelling, LLM evaluation frameworks, knowledge graphs, domain-specific ontologies, wearable sensors/IoT data.
Preferred skills
Experience with probabilistic evaluation, model calibration, model monitoring, causal inference, counterfactual modelling, simulation, LLM/AI evaluation frameworks.
Responsibilities
Design and build production data infrastructure for high-volume athlete performance and sensor data; Build and operate real-time and near-real-time ingestion systems for streaming data; Design storage and data architectures for high-volume time-series data and longitudinal athlete records; Build infrastructure that makes production features and derived metrics available to machine learning systems and AI agents with low latency; Design and implement graph data models and schemas representing relationships between athletes, training loads, injuries, performance, and outcomes; Build data and ML evaluation infrastructure that helps measure model reliability, calibration, and performance across real-world cases.
Seniority
Senior, hands-on IC
