(Senior) Machine Learning Engineer

{
  "content_hash": "a22d7ad63eaa024258b303dfe57b4a4522b2bbf4f67943b3d507825b8e53a129",
  "source_hash": "1d703f42386329e05aded4f890e7c16d0390b45e9ea701e516629741016010cd",
  "last_changed_at": "2026-05-30T05:54:30.608Z",
  "active_status": "active"
}

{
  "language": "en",
  "location": {
    "raw": "USA - RaceOn",
    "city": null,
    "region": null,
    "country": "United States",
    "is_remote": true,
    "confidence": 0.95
  },
  "salary_max": null,
  "salary_min": null,
  "inferred_at": "2026-06-06T07:58:32.692Z",
  "launch_scope": {
    "reason": "personio_production_catalog",
    "included": true,
    "location": {
      "raw": "USA - RaceOn",
      "city": null,
      "region": null,
      "country": "United States",
      "is_remote": true,
      "confidence": 0.95
    },
    "countries": [
      "United States"
    ]
  },
  "remote_policy": "remote",
  "salary_period": null,
  "workplace_type": "remote",
  "salary_currency": null
}

{}

{
  "id": "2554431",
  "name": "(Senior) Machine Learning Engineer",
  "office": "USA - RaceOn",
  "keywords": [],
  "schedule": "full-time",
  "createdAt": "2026-03-05T10:22:12+00:00",
  "seniority": "experienced",
  "department": "Inc. USA",
  "occupation": "general_and_other_engineering",
  "subcompany": null,
  "employmentType": "permanent",
  "jobDescriptions": [
    {
      "name": "General Information",
      "value": "RaceOn is seeking <strong>a Senior <strong>Machine Learning Engineer </strong></strong>as well as a <strong>Junior Machine Learning Engineer</strong> 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.<br><br>Any applications not matching minimum profile requirements will be ignored."
    },
    {
      "name": "Your mission",
      "value": "<ul><li>Develop, validate, and deploy ML models for performance and operational use cases (e.g., predictive analytics, decision support, performance measurement)</li><li>Build data pipelines and analysis workflows for structured and time-series data</li><li>Implement monitoring and iteration practices for deployed models (MLOps basics)</li><li>Collaborate with engineering and performance stakeholders to translate requirements into deliverables</li><li>Contribute to ML infrastructure and codebase quality (reviews, documentation, reusable components)</li><li>Travel occasionally for live validation and stakeholder feedback (role dependent; approx. 5–6 race weekends/year for some assignments)</li></ul>"
    },
    {
      "name": "Your profile",
      "value": "<ul><li>2+ years building production ML systems</li><li>MSc in Machine Learning, Data Science, Computer Science, or related field (or equivalent experience)</li><li>Strong <strong>Python</strong> and experience with ML libraries (scikit-learn and/or PyTorch/TensorFlow)</li><li>Experience with data handling and querying (<strong>SQL</strong>)</li><li>Understanding of model evaluation, deployment concepts, and version control (Git)</li><li>Ability to work in complex engineering environments and communicate with non-ML stakeholders</li><li>Advantageous would be: time-series forecasting, optimization, real-time systems, dashboards, sports/motorsport analytics, AWS experience.</li></ul><strong><span style=\"font-size:14px;\">Work Location</span><span style=\"font-size:14px;\"> </span><br>USA</strong> | Remote possible (role-dependent) | Limited Travel required<br><br><br>"
    }
  ],
  "occupationCategory": "engineering",
  "recruitingCategory": "Permanent Employee"
}