Staff Tech Lead Manager, ML Data Services

About the role

Motional is seeking a Staff Technical Leader to manage the critical infrastructure and data pipelines that power our machine learning initiatives, owning the end-to-end lifecycle of data services essential for advancing autonomous vehicle technology. In this role, you will lead the strategic direction and operational execution of the ML Data Services team, ensuring that our data platforms meet the rigorous demands of cutting-edge research and production systems. You will own the design, implementation, and reliability of the large-scale data processing systems that transform raw sensor information into actionable training data for our driving algorithms. This position requires a hands-on technical manager who is deeply involved in both architectural decisions and the day-to-day health of the data ecosystem, working closely with engineers and scientists to remove impediments and drive efficiency. The hire will act as the primary technical owner for the data services layer, ensuring that pipelines are robust, scalable, and aligned with the broader product and research objectives of the organization. You will provide technical leadership in building data solutions that are not only performant today but are also sustainable and extensible for the long-term roadmap of autonomous driving. This role is pivotal in bridging the gap between ambitious research experiments and deployable, production-grade data infrastructure. Ultimately, the Staff Tech Lead Manager will ensure that Motional's data services are a source of competitive advantage, enabling faster iteration and higher-quality models in a safety-critical environment.

What you'll do

• Lead a team of engineers in the development, deployment, and maintenance of scalable data pipelines that feed directly into machine learning model training and validation workflows.
• Architect and evolve distributed data systems that can handle the massive volume, velocity, and variety of sensor data generated by autonomous vehicles in real-world conditions.
• Partner closely with research and engineering teams to identify data quality issues, define standards for dataset curation, and improve the overall accessibility of data assets.
• Provide hands-on technical mentorship and career development guidance to direct reports, fostering a culture of excellence, learning, and ownership within the ML Data Services team.
• Ensure the reliability, performance, and security of data services, implementing monitoring, alerting, and operational best practices to maintain high availability for critical workflows.
• Collaborate with cross-functional stakeholders to translate high-level product and research requirements into detailed technical specifications for data infrastructure.
• Evaluate and integrate new data processing technologies and frameworks, making strategic decisions that balance innovation with stability and operational simplicity.
• Drive the implementation of data processing frameworks that optimize for scalability, cost efficiency, and reproducibility across the entire machine learning lifecycle.
• Champion data governance and documentation practices to ensure that datasets are well understood, traceable, and usable across different teams and experiments.
• Lead incident response efforts for data platform outages or degradation, working with engineering teams to conduct thorough postmortems and implement preventative measures.
• Contribute to the technical roadmap for ML Data Services, balancing short-term needs with long-term strategic investments in platform capabilities.
• Act as a technical leader in the absence of senior management, representing the ML Data Services team in broader engineering and product discussions.
• Promote engineering best practices such as code review, testing, and continuous integration specific to data pipeline development and deployment.
• Build and maintain strong working relationships with other teams to ensure alignment on data priorities and to identify opportunities for collaboration.

Requirements

• Proven experience in a technical leadership or management role within a machine learning or data engineering environment, with a track record of successfully delivering complex data platforms.
• Deep expertise in building, scaling, and operating data infrastructure for complex AI or ML systems, including familiarity with distributed storage and compute paradigms.
• Strong proficiency in software engineering principles, system design patterns, and modern data architectures such as data lakes, data warehouses, and streaming platforms.
• Proven ability to bridge the gap between high-level research goals and practical engineering execution, translating ambiguous problems into robust technical solutions.
• Experience managing and mentoring technical professionals, including conducting performance reviews, providing feedback, and supporting professional growth.
• Solid understanding of machine learning workflows, including data preprocessing, feature engineering, model training, and validation processes.
• Strong communication skills, capable of conveying complex technical concepts to both technical and non-technical stakeholders effectively.
• Experience working in fast-paced, agile environments where priorities can change rapidly and adaptability is essential.
• Commitment to writing clean, maintainable code and ensuring that data pipelines are observable, testable, and resilient.
• Willingness to engage in hands-on technical work, including debugging difficult data pipeline issues and reviewing critical architecture designs.