Machine Learning Engineer, Data Mining

Motional

3d•$144,000 - $192,000•Hybrid

About The Position

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.

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.

Nice To Haves

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)

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.