Principal Engineer, Data & ML Infrastructure

Motional is a driverless technology company developing autonomous vehicles. The Principal Engineer, Data & ML Infrastructure will lead the Ground Truth Regression team in building large-scale data analysis, orchestration, and infrastructure systems that evaluate autonomous vehicle ML models and software. The role also provides technical leadership, mentorship, statistical analysis, and project oversight.

Responsibilities:

• Lead a team of engineers with a diverse skill set to deliver critical software and infrastructure supporting the development of the autonomous vehicle ML models and software
• Drive innovation by researching and developing new large scale data warehousing and analysis
• Own large-scale data analysis workflows that surface unexpected impacts of planned and unplanned changes to the ML models and software
• Work with annotation specialists to build high-quality evaluation datasets
• Provide statistical depth on model performance & generalization through rigorous error and change analysis
• Provide technical leadership and mentorship to the team, fostering a culture of collaboration and continuous improvement
• Manage project timelines, resources, and deliverables to ensure successful project execution

Requirements:

• • Bachelor's or Master's degree in Computer Science, Data Science, or a related field
• • Excellent leadership, communication, and problem-solving skills
• • Proven record leading a small diverse dedicated team supporting a large user base
• • Experience in machine learning, data mining, and statistical analysis
• • Proficiency in Go (Golang), Python, or C++ for building high-performance, concurrent microservices and platform tooling
• • Deep hands-on experience building production infrastructure on public clouds (AWS, GCP, or Azure) and managing containerized workloads at scale using Kubernetes (K8s)
• • Experience with large-scale data processing and distributed computing technologies
• • Ph.D. in Computer Science or a related field
• • Knowledge of or hands-on experience with OLAP databases (e.g., ClickHouse) for data analysis
• • Experience with machine learning in the autonomous driving domain
• • Familiarity with error analysis and statistical techniques for monitoring model performance and generalization in production
• • Publications or contributions to the AI/ML community