Engineering Manager, Machine Learning Behavior Planning & Prediction

TEAM

The Behavior team at Woven by Toyota tackles Autonomy challenges for problems in prediction and trajectory planning. Our work involves a variety of challenges, such as analyzing petabytes of multimodal driving data, solving optimization problems, minimizing latency on hardware accelerators, deploying scalable and efficient machine learning (ML) training and evaluation pipelines, and designing novel neural network architectures to advance state-of-the-art ML for Prediction and Motion Planning.

WHO ARE WE LOOKING FOR?

We are seeking an experienced and technically strong, hands-on Engineering Manager to lead a group of highly talented and passionate engineers in the behavior team. This role is focused on developing and deploying state-of-the-art machine learning models for planning and prediction in AD/ADAS, along with their associated data processing pipelines and vehicle deployment. The ideal candidate has a deep passion for self-driving technology and its potential global impact. You will lead your team in advancing our core behavior machine learning models and accompanying pipelines. We are looking for an individual with domain expertise in machine learning, a comprehensive understanding of the model lifecycle, and experience developing tools and pipelines for behavior problems. Success in this role requires a candidate who thrives in a fast-paced environment, is hands-on, and is eager to push the boundaries of our technology. Prior experience building and deploying real-world autonomous driving products, collaborating with highly talented engineers, is essential.

RESPONSIBILITIES

• Define the team's short-term and long-term technical direction while collaborating on broader, cross-functional strategic initiatives.
• Initiate and influence cross-functional teams towards common development goals.
• Enable and help other engineers on the team to be more effective through coaching and leading by example and by providing high-quality code and design document reviews and delivering rigorous reports from ML experiments.
• Be actively involved in day-to-day development and code review.
• Collaborate with team members to design, develop, deploy, and evaluate state of the art algorithms and capabilities for prediction and motion planning.
• Use metrics to measure, validate, and improve performance through testing in simulation and on roads.
• Design reusable software components as part of an integrated system.
• Understand and fulfill the software practices that produce maintainable code, including simulation, continuous integration, code review, HIL testing, and in-vehicle testing.
• 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

• M.S., Ph.D., or equivalent, in Computer Science, Robotics, Control, Applied Mathematics, or other quantitative fields.
• 3+ years of experience managing an engineering team, both in people management and technical leadership. A strong candidate should have demonstrated strong technical leadership, team development, and delivered high-impact projects in the automotive industry.
• 5+ years of experience with learning-based planning approaches such as supervised/unsupervised imitation learning, transfer learning, multi-task learning, reinforcement learning, and state-of-the-art techniques for sequential modeling like Transformer architectures
• 5+ years of experience covering machine learning workflows, data sampling and curation, pre-processing, model training, ablation studies, evaluation, deployment, and inference optimization.
• Passionate about self-driving car technology and its potential for humanity.
• Strong communication skills with the ability to communicate concepts clearly and precisely
• Strong programming skills in Python.

NICE TO HAVES

• Published research at top-tier conferences (NeurIPS, CVPR, RSS, IROS, ICRA, and similar).
• 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 with temporal data and/or sequential modeling.
• Experience in self-driving challenges (Perception, Prediction, Planning, Simulation).