[Hiring] Machine Learning Engineer, Data Mining @Motional

Role Description

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

Qualifications

• BS or MS in Computer Science, Machine Learning, or a related field.
• Hands-on experience with PyTorch (preferred) or TensorFlow/JAX. 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.

Requirements

• MS/PhD in Computer Science, Machine Learning, or related field (Bonus Points).
• Experience with agentic systems, autonomous reasoning, chain-of-thought models, or LLM-based planning (Bonus Points).
• Background in autonomous driving, robotics, or real-time decision-making systems (Bonus Points).
• Familiarity with multimodal learning, sensor fusion, or embodied AI (Bonus Points).
• Experience building active learning loops, using the model to find the data that breaks the model (Bonus Points).
• Experience with ML-based data mining, active learning, or contrastive learning (Bonus Points).
• Knowledge of model serving tools (TF Serving, Triton, TorchServe) and MLOps platforms (Bonus Points).
• Publication in top-tier conferences (e.g., ICCV, CVPR, ECCV) (Bonus Points).

Benefits

• Medical, dental, vision coverage.
• 401k with a company match.
• Health saving accounts.
• Life insurance.
• Pet insurance.
• And more.

Salary Range

$144,000 — $192,000 USD