Note: The job is a remote job and is open to candidates in USA. Motional is a driverless technology company focused on transforming transportation through autonomous vehicles. As a Machine Learning Engineer on the Data Mining team, you will build the 'Brain' of the ML-powered data mining framework, developing tools and pipelines to enhance model improvement and data insights.
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
Skills
- 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
- 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)
Benefits
- Medical
- Dental
- Vision
- 401k with a company match
- Health saving accounts
- Life insurance
- Pet insurance
Company Overview