RaceOn

• Lead and mentor the Performance Engineering team • Oversee performance analysis strategies and reporting • Drive cross-functional collaboration with Race Engineers and Drivers • Lead the calibration and optimization of vehicle dynamics and performance-related on-car systems • Direct race car setup and performance optimization • Establish and monitor key performance indicators • Develop and refine advanced performance software tools

General Information RaceOn is seeking a Senior Machine Learning Engineer as well as a Junior Machine Learning Engineer to architect and build production ML systems that create a competitive advantage on race day. The tasks are slightly different but candidates will be managed via the same application and more details will be available in the interview. A generalization of tasks is listed below as well as profile requirements. Any applications not matching minimum profile requirements will be ignored. Your profile - 2+ years building production ML systems - MSc in Machine Learning, Data Science, Computer Science, or related field (or equivalent experience) - Strong Python and experience with ML libraries (scikit-learn and/or PyTorch/TensorFlow) - Experience with data handling and querying (SQL) - Understanding of model evaluation, deployment concepts, and version control (Git) - Ability to work in complex engineering environments and communicate with non-ML stakeholders - Advantageous would be: time-series forecasting, optimization, real-time systems, dashboards, sports/motorsport analytics, AWS experience. Work Location USA | Remote possible (role-dependent) | Limited Travel required Why us? Your mission - Develop, validate, and deploy ML models for performance and operational use cases (e.g., predictive analytics, decision support, performance measurement) - Build data pipelines and analysis workflows for structured and time-series data - Implement monitoring and iteration practices for deployed models (MLOps basics) - Collaborate with engineering and performance stakeholders to translate requirements into deliverables - Contribute to ML infrastructure and codebase quality (reviews, documentation, reusable components) - Travel occasionally for live validation and stakeholder feedback (role dependent; approx. 5–6 race weekends/year for some assignments)