(Senior) Machine Learning Engineer

Machine Learning EngineerMachine Learning EngineerFull TimeRemoteTeam 11

Location

United States + 1 moreAll locations: United States, Germany

Posted

8 days ago

Salary

Not specified

No structured requirement data.

Job Description

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)

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