Principal Engineer, Data & ML Infrastructure

About the role

Motional is looking for a Principal Engineer focused on Data and ML Infrastructure to help build the systems that underpin their autonomous driving platform. This senior individual contributor role centers on designing, developing, and maintaining the data and machine learning infrastructure that enables teams to train, deploy, and monitor models at scale. The engineer will partner with data scientists, ML engineers, and software developers to understand infrastructure needs and deliver reliable, high-performance data services. This position plays a key part in shaping the technical direction of Motional's data platform and ensuring it can support the growing demands of autonomous vehicle research and deployment.

Key facts

What you'll do

• Architect and build data pipelines that process large volumes of information from autonomous vehicle sensors and telemetry systems.
• Design and operate platforms that enable machine learning teams to train models, run experiments, and deploy models to production.
• Work with cross-functional teams to understand their data needs and deliver infrastructure solutions that are reliable and performant.
• Take ownership of the stability and scalability of distributed data storage and processing systems used across the organization.
• Create monitoring and alerting systems for ML workflows to identify failures, bottlenecks, and data quality problems early.
• Guide technical decisions for data and ML infrastructure projects, ensuring they align with the company's long-term engineering goals.
• Provide mentorship and technical guidance to engineers at all levels working on data and ML platform teams.
• Improve the efficiency of data processing workflows to reduce latency and increase throughput for both batch and real-time workloads.
• Assess and integrate new tools and frameworks that can strengthen the data and ML infrastructure ecosystem.
• Coordinate with cloud and infrastructure teams to ensure on-premises and cloud environments work together effectively.
• Establish data standards and practices that promote consistency, quality, and governance across all data assets used in ML.
• Participate in on-call responsibilities to ensure production data systems remain available and issues are resolved quickly.

Requirements

• Extensive experience in data engineering, ML infrastructure, or a closely related technical discipline at a senior level.
• Proficiency in programming languages commonly used for data systems and machine learning platform development.
• Strong understanding of distributed computing concepts and experience with frameworks for both stream and batch data processing.
• Hands-on experience designing and operating large-scale data storage systems, including data lakes and feature stores.
• Familiarity with container orchestration and cluster management technologies used to run ML workloads.
• Background in constructing and maintaining ML pipelines, including model training orchestration and experiment tracking.
• A degree in Computer Science, Engineering, or a related technical field, or equivalent years of practical experience.
• Demonstrated success collaborating with data scientists, ML engineers, and software engineers in a team-oriented setting.
• Working knowledge of major cloud computing platforms and their data and machine learning offerings.
• Excellent communication abilities with a track record of presenting technical designs and influencing engineering decisions.

Nice to have

• Background in autonomous vehicle technology or robotics, particularly with sensor data and perception systems.
• Familiarity with MLOps methodologies and tooling for managing the full machine learning lifecycle.
• Contributions to open-source projects in the data engineering or ML infrastructure space are valued.
• Experience with real-time data processing and low-latency model serving architectures in production environments.
• Knowledge of data security and privacy considerations for handling sensitive automotive and geospatial information.

Skills & tools

• Programming languages used for data engineering and ML platform development work
• Distributed data processing and streaming frameworks
• Container orchestration platforms for deploying ML workloads
• Data lake and feature store design patterns
• Major cloud computing providers and their ML and data services
• Systems programming languages for building performance-sensitive infrastructure

Practical notes

• This position offers the flexibility of being based in Boston, Massachusetts, Pittsburgh, Pennsylvania, or Remote U.S.
• As a principal-level role, the engineer will have broad influence over the direction of data and ML infrastructure at Motional.
• The role may involve travel to team offices or industry events for collaboration and professional development purposes.
• Motional is an equal opportunity employer and welcomes applicants from all backgrounds.