Your Role & Responsibilities Own the end-to-end data lifecycle—from collection and annotation to quality assurance, governance, and cloud storage—ensuring high-quality datasets feed into model training pipelines.
Define the dataset strategy and roadmap to support scalable and diverse training data for advanced machine learning models in close collaboration with research teams.
Architect and continuously improve a robust cloud-based data platform (including ingestion, labeling workflows, cataloging, versioning, and compliance processes).
Operationalize the data lifecycle with clear processes, KPIs, and feedback loops spanning data collection, model training, and deployment.
Lead and mentor a high-performing data team, fostering technical excellence, ownership, and continuous professional growth.
Partner closely with Product, Research, and Engineering teams as the central point of contact for data strategy and data-related initiatives.
Collaborate with robotics and systems teams to support data collection and integration workflows within robotics environments (e.g., ROS2-based systems).
Required Technical & Professional Expertise 3+ years of experience leading or managing technical teams in a fast-paced environment.
5+ years of professional experience in data engineering, machine learning systems, cloud infrastructure, or related domains.
An advanced degree (Master’s or PhD) in Computer Science, Machine Learning, Data Engineering, or a related field.
Proven ability to design and scale large-scale data pipelines, data platforms, or cloud-based storage systems.
Experience building or managing datasets for deep learning systems, ideally including multimodal data.
Experience with data lifecycle management, data collection operations, or high-throughput cloud data platforms is a strong advantage.
Strong foundations in Python, cloud data tooling, and modern data/ML engineering practices.
Experience working within robotics ecosystems (ROS2 or similar frameworks) is beneficial but not required.
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