ML for sports performance

Andersen is hiring an ML/Data Engineer for a project developing an AI-powered sports performance platform and delivering scalable data solutions and real-time insights for athletes and coaches.

• Designing and building production data infrastructure for high-volume athlete performance and sensor data.
• Building and operating real-time and near-real-time ingestion systems for streaming data.
• Designing storage and data architectures for high-volume time-series data and longitudinal athlete records.
• Building infrastructure that makes production features and derived metrics available to machine learning systems and AI agents with low latency.
• Designing and implementing graph data models and schemas representing relationships between athletes, training loads, injuries, performance, and outcomes.
• Building data and ML evaluation infrastructure that helps measure model reliability, calibration, and performance across real-world cases.
• Designing systems that maintain strong tenant-level data isolation across clubs and customers.
• Establishing appropriate data provenance, lineage, auditability, and observability across the platform.
• Working with ML and AI engineers to provide reliable data foundations for model training, inference, and evaluation.
• Working with sport scientists and domain experts to translate complex requirements into durable production systems.
• Making pragmatic technology and architecture decisions as the platform evolves.

• Full-time professional software or data engineering for 5+ years, excluding internships, university placements, coursework, and academic projects.
• Proven experience designing, building, and operating production data infrastructure at scale.
• Strong experience with time-series data or time-series databases (InfluxDB, TimescaleDB, Prometheus, ClickHouse, or equivalent).
• Significant experience with real-time or streaming data ingestion (Kafka, Kinesis, Flink, Spark Streaming, Pulsar, or equivalent).
• Experience designing graph data models or graph database schemas, not only querying an existing graph database.
• Experience designing or operating multi-tenant systems with tenant-level data isolation.
• Strong Python and SQL.
• Experience with production systems where reliability, scalability, observability, and data correctness matter.
• Ability to own ambiguous technical problems and turn them into practical production architecture.
• Experience working directly with data scientists, ML engineers, or other technical domain specialists.
• Level of English – from Upper-Intermediate+ and above.
• Будет плюсом: Professional experience with Go; Experience building probabilistic evaluation, model calibration, or model monitoring infrastructure; Experience with causal inference, counterfactual modelling, or simulation; Understanding of wearable sensors, IoT data, biomechanics, sports technology, or other high-frequency telemetry; Knowledge graphs or domain-specific ontologies; LLM or AI evaluation frameworks and an understanding of their limitations; Experience with AWS, including ECS, EC2, Lambda, SNS, or SQS; Experience with GraphQL, REST, gRPC, Postgres, MongoDB, or similar technologies

• The opportunity to change the project and/or develop expertise in an interesting business domain.
• Job conditions – you can work both fully remotely and from the office or can choose a hybrid variant.
• Guarantee of professional, financial, and career growth!
• The company has introduced systems of mentoring and adaptation for each new employee.
• The opportunity to earn up to an additional 1,000 EUR per month, depending on the level of expertise, which will be included in the annual bonus, by participating in the company's activities.
• Access to the corporate training portal, where the entire knowledge base of the company is collected and which is constantly updated.
• Bright corporate life (parties / pizza days / PlayStation / fruits / coffee / snacks / movies).
• Certification compensation (AWS, PMP, etc).
• Referral program.
• Private health insurance and sports compensation, depending on the type of employment.