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, powers this discovery. As a Machine Learning Engineer on the Data Mining team, your mission is to help build the "Brain" of this engine, working with state-of-the-art foundation models to extract insights from Motional’s driving data at the intersection of large-scale representation learning and data retrieval.

By building smarter mining tools and efficient data pipelines, you’ll accelerate the model improvement lifecycle for teams working on post-training analysis, error diagnosis, and dataset curation.

• Build and train machine learning pipelines: develop, train and fine-tune models for multimodal sensor data (e.g., vision, LiDAR), implementing supervised and self-supervised learning approaches to improve data search and retrieval.
• Support model deployment: implement scalable data preprocessing and augmentation pipelines and apply optimization techniques such as batch inference and quantization to ensure models run efficiently in production environments.
• Data mining & analysis: develop embedding-based search tools and active learning workflows to identify critical driving scenarios.
• Monitor production performance: build and maintain dashboards to monitor model health, data drift, and system performance, identify regressions and assist operational support of 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 technical documentation.
• Collaborate across teams: work closely with senior engineers and machine learning engineers to translate model prototypes into maintainable, scalable engineering solutions.

• BS or MS in Computer Science, Machine Learning, or a related field.
• Hands-on experience with PyTorch (preferred) or Tensor Flow/JAX; comfortable training models and evaluating them with standard metrics.
• Strong proficiency in Python and ability to write clean, modular, well-documented code.
• Working knowledge of version control, unit testing, and basic software design patterns.
• Experience with large datasets, proficiency in SQL and data libraries like Pandas and Num Py.
• Solid grasp of the full ML lifecycle: data cleaning, feature engineering, validation, and deployment basics.
• Proactive learner, thrives on constructive feedback and 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 models to find data that breaks them.
• Experience with ML-based data mining, active learning, or contrastive learning.
• Knowledge of model serving tools (TF Serving, Triton, Torch Serve) and MLOps platforms.
• Publications in top-tier conferences (e.g., ICCV, CVPR, ECCV).

Location:

Boston, Las Vegas, Pittsburgh, or Remote U.S. only.

Salary Range: $144,000 – $192,000 USD.

Benefits include medical, dental, vision, 401(k) with company match, health savings accounts, life insurance, pet insurance, and more.

Motional AD Inc. is an EOE. We celebrate diversity and are committed to creating an inclusive environment for all employees. To comply with Federal Law, we participate in E‑Verify.