Machine Learning Engineer, Data Mining

Motional is a driverless technology company focused on transforming transportation through autonomous vehicles. As a Machine Learning Engineer on the Data Mining team, you will build the 'Brain' of the ML-powered data mining framework, developing tools and pipelines to enhance model improvement and data insights.

Responsibilities:

• 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 technical documentation
• Collaborate Across Teams: Work closely with senior engineers and machine learning engineers to translate model prototypes into maintainable, scalable engineering solutions

Requirements:

• BS or MS in Computer Science, Machine Learning, or a related field
• Hands-on experience with PyTorch (preferred) or TensorFlow/JAX. You should be comfortable training models and evaluating them using standard metrics
• Strong proficiency in Python with the ability to write clean, modular, and well-documented code
• Working knowledge of version control, unit testing, and basic software design patterns
• Experience working with large datasets, including proficiency in SQL and data libraries like Pandas and NumPy
• A solid grasp of the full ML lifecycle, from data cleaning and feature engineering to validation and deployment basics
• A proactive learner who thrives on constructive feedback and is eager to grow within a high-stakes engineering environment
• MS/PhD in Computer Science, Machine Learning, or related field
• Experience with agentic systems, autonomous reasoning, chain-of-thought models, or LLM-based planning
• Background in autonomous driving, robotics, or real-time decision-making systems
• Familiarity with multimodal learning, sensor fusion, or embodied AI
• Experience building active learning loops, using the model to find the data that breaks the model
• Experience with ML-based data mining, active learning, or contrastive learning
• Knowledge of model serving tools (TF Serving, Triton, TorchServe) and MLOps platforms
• Publication in top-tier conferences (e.g., ICCV, CVPR, ECCV)