Machine Learning Engineer, Data Mining

Job Description

Mission Summary

Motional is transforming how autonomous vehicles discover critical intelligence hidden within petabytes of multimodal sensor data. Omnitag, powered by ML-powered multimodal data mining framework, enables teams to find rare edge cases, long-tail scenarios, and model errors.

What You'll Do

• 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.

What We're Looking For (Must-Haves)

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

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 or contrastive learning.
• Knowledge of model serving tools (TF Serving, Triton, TorchServe) and MLOps platforms.
• Publishation in top-tier conferences (e.g., ICCV, CVPR, ECCV).

Location & Schedule

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

Salary Range

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

Benefits

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

Ready to Apply?

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