Motional

Machine Learning Engineer, Data Mining

Boston, Massachusetts, United States; Remote U.S

Sponsorship not specified$144k-$192kDetected 28 days ago

PythonCode ReviewSQLCI/CDMachine LearningTensorFlowPyTorchPandasData EngineeringLLMsAgentic AIMLOpsRoboticsSensorsCollaboration

About the role

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.

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.
Working knowledge of version control, unit testing, and basic software design patterns.
Identify regressions and assist in the operational support of our data mining services.

Strong proficiency in Python with the ability to write clean, modular, and well-documented code.
Experience working with large datasets, including proficiency in SQL and data libraries like Pandas and NumPy.

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.
Knowledge of model serving tools (TF Serving, Triton, TorchServe) and MLOps platforms.
Publication in top-tier conferences (e.g., ICCV, CVPR, ECCV)
Hands-on experience with PyTorch (preferred) or TensorFlow/JAX.
You should be comfortable training models and evaluating them using standard metrics.

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.

$144,000 - $192,000 USD
Motional is a driverless technology company making autonomous vehicles a safe, reliable, and accessible reality. We're driven by something more.
Our journey is always people first.
We aren't just developing driverless cars
we're creating safer roadways, more equitable transportation options, and making our communities better places to live, work, and connect.
Our team is made up of engineers, researchers, innovators, dreamers and doers, who are creating a technology with the potential to transform the way we move.

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.
Help build and maintain dashboards to monitor model health, data drift, and system performance.
Data Mining & Analysis: Help develop embedding-based search tools and "active learning" workflows to identify critical driving scenarios.
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.
Bonus Points (Nice-to-Haves):
MS/PhD in Computer Science, Machine Learning, or related field.
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.
This role may include additional forms of compensation such as a bonus or company equity.

This listing is sourced directly from Motional's careers page and normalized into a canonical job model.