Senior Data Engineering Manager, AD/ADAS

Enable and develop each team member to be more effective through coaching and leading by example providing high-quality algorithms, carefully thought out design reviews, and delivering rigorous initiative reports.

Collaborate and provide technical guidance in the design, development, deployment, optimization, and evaluation of state‑of‑the‑art data algorithms, data efficiency and pipelines to enable ML model performance with a focus on iteration speed, scalability and overall cost efficiency.

Drive a culture of rigorous statistical evaluation to measure, validate and implement evaluation frameworks that support ML model development and improved system behavior.

Drive organizational metrics towards performance, safety, and quality.

Work in a high-velocity environment, employ agile development practices, and collaborate in a globally distributed department (US, Japan, UK).

Masters or PhD degree in Machine Learning, Computer Science, Robotics, Applied Mathematics, Statistics, or related quantitative fields, or equivalent industry experience

3+ years of experience managing engineering teams, with a focus on technical leadership, team development, and delivering high-impact projects in the automotive industry.

8+ years of experience covering data science, data sampling and curation, machine learning workflows, pre-processing, model training, ablation studies, evaluation, deployment, and inference optimization.

Experience with rigorous data science for applications leveraging large scale data sources and highly efficient pipelines

Experience working with large-scale robotics datasets, temporal data and/or sequential modeling

Excellent communication skills with the ability to communicate concepts clearly and precisely

Experience writing software using: i) Python for data science (numpy, scipy, scikit, pandas; ii) databases; and iii) cloud platform services (AWS, GCP, Azure)

Experience as a Data Architect or Senior Manager

Proven track record of deploying ML models at scale in self-driving products, self-driving challenges, or related fields

Experience with production data pipelines or Safety & Quality processes for safety-critical applications

Hands-on experience in the following:

Analyze huge (e.g. peta byte) scale database;

Develop perception-related technologies, such as camera image processing, computer vision, machine learning, or deep learning;

Design data annotation rules for machine learning

Build or manage infrastructure, such as Docker, Kubernetes, Jenkins, GitHub Actions

Behavior prediction, imitation learning, reinforcement learning, or end-to-end models.

Japanese language skills