Senior Machine Learning Engineer, Modeling & Pipelines, Behavior Planning

RESPONSIBILITIES

• Design, build, and maintain ML pipelines that produce training and evaluation data for behavior and prediction models, including ETL, feature extraction, augmentation, and labeling workflows.
• Implement auxiliary pipelines for model validation, metric computation, and on-road performance integration.
• Design and develop advanced machine learning models in the behavior space, specifically tailored for autonomous vehicles, utilizing deep learning and large-scale data analysis.
• Deploy scalable and efficient ML models on our autonomous vehicle platform.
• Integrate modern technologies with rigorous safety standards while maintaining cost efficiency.
• Oversee the development of new ML models end-to-end, from data strategy and initial development to optimization, production platform validation, and fine-tuning based on metrics and on-road performance.
• Lead large, multi-person projects and significantly influence the overall motion planning architecture and technical direction.
• Enable and support other engineers by coaching, leading by example, and providing high-quality code and design document reviews, as well as delivering rigorous reports from ML experiments.
• Contribute significantly to the development of essential components for end-to-end ML training and deployment, from data strategy to optimization and validation.
• Be a champion of the scientific method and critical thinking to invent state-of-the-art deep learning solutions.
• Work in a high-velocity environment and employ agile development practices.
• Collaborate closely with teams such as Perception, Simulation, Infrastructure, and Tooling to drive unified solutions.

MINIMUM QUALIFICATIONS

• MS or PhD in Machine Learning, Computer Science, Robotics, or related quantitative fields, or equivalent industry experience.
• 3+ years of experience with Python, major deep learning frameworks, and software engineering best practices.
• Built robust data pipelines, ETL workflows, and integration systems to support behavior prediction models
• Expertise with deep learning approaches, such as supervised/unsupervised learning, transfer learning, multi-task learning, and deep reinforcement learning.
• Good understanding of learning-based planning approaches, including imitation learning, reinforcement learning, and state-of-the-art techniques for sequential modeling, such as Transformer architectures.
• Practical experience with ML training and evaluation workflows, including data sampling, preprocessing, and reproducible experiment management.
• 3+ years of experience covering machine learning workflows, data sampling and curation, preprocessing, model training, ablation studies, evaluation, deployment, and inference optimization.
• Comfortable writing C++ code for integration with our autonomous vehicle platform.
• Passion for self-driving car technology and its potential to impact humanity.
• Strong communication skills with the ability to articulate concepts clearly and precisely.

NICE TO HAVES

• Published research at top-tier conferences (NeurIPS, RSS, IROS, ICRA, etc.).
• Proven track record of deploying ML models at scale in self-driving or related fields.
• Familiarity with production-level coding in time-limited task schedules.
• Experience with robot motion planning (e.g., trajectory optimization, sampling-based planning, model predictive control).
• Experience working with temporal data and sequential modeling.
• Expertise in self-driving challenges (Perception, Prediction, Planning, Simulation).