Senior Data Engineering Manager, AD/ADAS

Overview

You lead the Autonomy Data team to design and deploy data-driven Perception and Planning solutions for Toyota’s autonomous driving initiatives. You shape the data strategy, curate large-scale datasets, and coach engineers to deliver high-impact ML workflows. Your work integrates data science with safety standards to improve model performance while controlling cost. This role sits at the crossroads of AI, robotics, and software engineering, offering platform-level impact across a global, fast-moving team.

Pay / Benefits

• Excellent health, wellness, dental and vision coverage
• A rewarding pension
• Flexible vacation policy
• Family planning and care benefits

Responsibilities

• Define and steer the team's short- and long-term technical direction across Perception, Planning, Simulation, Infrastructure, and Tooling
• Drive cross-functional alignment to achieve unified data development goals
• Develop and connect data roadmap—from collection, labeling, sampling, evaluation to production-to-improve end-to-end ML pipeline performance
• Lead initiative execution, manage risks, and drive long-term improvements
• Coach and develop team members with high-quality algorithms, design reviews, and rigorous initiative reporting
• Provide technical guidance on data algorithms, efficiency, pipelines, and deployment with focus on speed, scalability, and cost
• Establish rigorous evaluation frameworks to drive ML model development and system reliability
• Contribute to organizational metrics around performance, safety, and quality
• Operate in a high-velocity, agile environment with global collaboration (US, Japan, UK)

Key requirements

• Masters or PhD in ML, CS, Robotics, Applied Mathematics, Statistics, or related quantitative field, or equivalent industry experience
• ≥3 years managing engineering teams with technical leadership and delivering impactful automotive projects
• ≥8 years in data science, data curation, ML workflows, preprocessing, training, evaluation, deployment, and inference optimization
• Experience with large-scale data sources and efficient data pipelines
• Experience with large-scale robotics datasets, temporal data, and sequential modeling
• Excellent communication skills
• Proficiency in Python for data science, databases, and cloud platforms (AWS, GCP, Azure)

• strong communication
• coaching and mentorship
• cross-functional collaboration
• Python data science stack (numpy, scipy, scikit, pandas)
• databases
• cloud platforms (AWS, GCP, Azure)