Engineering Manager, Machine Learning Behavior Planning & Prediction

Team: AD/ADAS

Location: Palo Alto, CA

Commitment: Employee

Workplace Type: hybrid

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).