Auto-labelling & Data Selection
My professional work and research focuses on scalable auto-labelling and efficient data selection for multi-modal data.
Research and Professional Work Focus
My work centers on the development of data-centric and machine learning systems for automated driving, combining applied research, large-scale software engineering, and technical leadership. Throughout my career, I have focused on transforming research concepts into scalable production systems that support perception development, data curation, ground truth generation, and validation workflows for autonomous vehicle technologies. A key aspect of my work has been close collaboration with research teams, enabling the translation of emerging machine learning methodologies into robust engineering platforms that can operate at industrial scale.
As Technical Lead for Data Selection in Video Perception, I lead the development of cloud-based systems that support intelligent selection and prioritization of large-scale sensor datasets. Working closely with perception researchers and machine learning scientists, I help evaluate and operationalize novel approaches for data-driven development, active learning, and dataset optimization. My responsibilities span both technical architecture and organizational leadership, including roadmap definition, stakeholder alignment, and the management of distributed international engineering teams. By bridging research and platform development, I have enabled the deployment of scalable data selection capabilities that improve the efficiency of model training, validation, and continuous improvement processes.
In parallel, I serve in a senior engineering leadership role for Ground Truth and Data Platforms, providing technical and strategic direction across a multi-team ecosystem supporting automated driving development. This role involves coordinating activities across research, platform engineering, data operations, and product organizations, ensuring alignment along the entire data and labeling value chain. I work closely with domain experts and research groups to identify emerging requirements, define scalable platform architectures, and establish technical standards that enable rapid experimentation while maintaining production reliability. In addition, I drive cross-organizational initiatives related to governance, KPI-driven performance management, platform standardization, and long-term technology strategy.
Prior to these leadership positions, I worked as a Deep Learning Engineer, developing machine learning solutions for perception and sensor data processing. In this role, I collaborated directly with research teams to implement, evaluate, and deploy deep learning approaches within large-scale development environments. This experience provided a strong foundation in machine learning, data engineering, and the practical challenges of bringing research innovations into real-world automotive applications.
Across all roles, I have consistently operated at the interface between research and engineering. My contributions have focused on enabling effective collaboration between scientists, machine learning experts, and software engineers, ensuring that promising research advances can be translated into scalable, maintainable, and impactful products. My professional interests include machine-learning-enabled data selection, large-scale data platforms, distributed cloud architectures, and organizational models that accelerate innovation in data-driven engineering. Through technical leadership, cross-functional collaboration, and platform development, I strive to create the foundations that allow research breakthroughs to deliver measurable impact in production autonomous driving systems.