Machine Learning Engineer, Data Mining

**Mission Summary:** At Motional, we're transforming how autonomous vehicles discover critical intelligence hidden within petabytes of multimodal sensor data. Our next\-generation autonomous driving stack depends on finding the rare edge cases, long\-tail scenarios, and model errors that matter most. Omnitag, our ML\-powered multimodal data mining framework, is the engine that powers this discovery. As a Machine Learning Engineer on the Data Mining team, your mission is to help build the "Brain" of this engine. You will work with state\-of\-the\-art foundation models to extract insights from Motional's driving data, working at the intersection of large\-scale representation learning and data retrieval. By building smarter mining tools and efficient data pipelines, you will accelerate the model improvement lifecycle for teams working on post\-training analysis, error diagnosis, and dataset curation. **What You'll Do:** * **Build and Train ML Pipelines:** Develop, train, and fine\-tune machine learning models for multimodal sensor data (e.g., vision, LiDAR). Focus on implementing supervised and self\-supervised learning approaches to improve data search and retrieval. * **Support Model Deployment:** Implement scalable data preprocessing and augmentation pipelines. Assist in applying standard optimization techniques (e.g., batch inference, quantization) to ensure models run efficiently in production environments. * **Data Mining \& Analysis:** Help develop embedding\-based search tools and "active learning" workflows to identify critical driving scenarios. * **Monitor Production Performance:** Help build and maintain dashboards to monitor model health, data drift, and system performance. Identify regressions and assist in the operational support of our data mining services. * **Learn and Apply Best Practices:** Follow software engineering standards (version control, CI/CD, unit testing) for ML code. Participate in code reviews and contribute to tec