Machine Learning Engineer, Data Mining

Job Description Machine Learning Engineer - Data Mining

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 rare edge cases, long-tail scenarios, and model errors. Omnitag, our ML-powered multimodal data mining framework, powers this discovery.

As a Machine Learning Engineer on the Data Mining team, you will work with state-of-the-art foundation models to extract insights from driving data at the intersection of large-scale representation learning and data retrieval. You will accelerate the model improvement lifecycle for teams working on post‑training analysis, error diagnosis, and dataset curation.

• Build and Train ML Pipelines:
  Develop, train, and fine‑tune machine learning models for multimodal sensor data. Focus on supervised and self‑supervised learning to improve data search and retrieval.
• Support Model Deployment:
  Implement scalable data preprocessing and augmentation pipelines. Apply optimization techniques like batch inference and quantization.
• Data Mining & Analysis:
  Develop embedding‑based search tools and "active learning" workflows to identify critical driving scenarios.
• Monitor Production Performance:
  Build dashboards to monitor model health, data drift, and system performance. Identify regressions and support data mining services.
• Learn and Apply Best Practices:
  Follow software engineering standards and participate in code reviews and documentation.
• Collaborate Across Teams:
  Work with senior engineers to translate model prototypes into maintainable solutions.

• BS or MS in Computer Science, Machine Learning, or related field.
• Hands‑on experience with PyTorch (preferred) or Tensor Flow/JAX.
• Strong proficiency in Python with clean, modular, well‑documented code.
• Working knowledge of version control, unit testing, and software design patterns.
• Experience with large datasets, SQL, Pandas, and Num Py.
• Solid grasp of the full ML lifecycle: data cleaning, feature engineering, validation, and deployment basics.
• Proactive learner thriving on constructive feedback in 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 or contrastive learning.
• Knowledge of model serving tools (TF Serving, Triton, Torch Serve) and MLOps platforms.
• Publication in top-tier conferences (e.g., ICCV, CVPR, ECCV).

Hybrid schedule with in‑office time in Boston, Pittsburgh, or Las Vegas, or fully remote.

$144,000 - $192,000 USD (base salary only; may include bonus or equity).

Medical, dental, vision, 401k with company match, health savings accounts, life insurance, pet insurance, and more.