ML Data Lead Engineer

Overview: As a Staff ML Data Engineer specializing in Autonomy Data Platform, you will lead the development of scalable data infrastructure that transforms large-scale autonomous driving fleet data into high-quality datasets for model training, simulation, and evaluation. You will define the technical architecture and drive the development of robust data ingestion, storage, processing, and curation pipelines capable of handling massive volumes of multimodal sensor data. Working closely with our autonomous driving, perception, simulation, and infrastructure teams, you will build the data foundation that enables rapid experimentation, continuous model improvement, and data-driven development of autonomous driving systems.

Responsibilities Lead autonomy data platform development, defining the technical roadmap and architecture for scalable data ingestion, storage, processing, and management of large-scale autonomous driving datasets. Design robust vehicle-to-cloud data pipelines, ensuring reliable ingestion, validation, and processing of multimodal fleet data, including camera, LiDAR, radar, and vehicle telemetry. Build and optimize large-scale data storage infrastructure, including data lakes, metadata indexing, efficient data formats, and dataset versioning to support training and evaluation workflows. Develop advanced data mining and curation tools, leveraging embedding-based retrieval, scenario classification, active learning, and intelligent sampling to identify valuable training and evaluation data. Establish data quality and governance frameworks, ensuring data consistency, traceability, validation, and reproducibility across the data lifecycle. Develop scalable data processing pipelines, supporting automated preprocessing, annotation workflows, pseudo-labeling, and dataset generation for large-scale machine learning. Build data exploration and visualization tools, enabling researchers and engineers to inspect fleet logs, analyze driving scenarios, and monitor dataset coverage across diverse driving conditions. Collaborate with autonomous driving, perception, simulation, and evaluation teams to define data requirements, streamline dataset delivery, and support efficient ML experimentation. Work closely with infrastructure and vehicle integration teams to optimize data transfer, storage efficiency, distributed processing, and platform reliability. Lead and mentor software and data engineers, providing technical guidance, driving architectural decisions, and establishing best practices in software engineering, data quality, and platform development. Explore emerging data infrastructure technologies, including scalable data processing, intelligent data mining, and ML-driven data management for autonomous driving

Requirements Minimum Qualifications 5+ years of experience in software engineering, data engineering, ML infrastructure, or related fields, with a strong track record of developing large-scale data systems. Bachelor's, Master's, or Ph.D. degree in Computer Science, Software Engineering, Electrical Engineering, or a related discipline, or equivalent practical experience. Strong expertise in designing and building scalable data pipelines, distributed data processing systems, and large-scale storage infrastructure. Solid understanding of modern data architectures, including data lakes, columnar storage formats, metadata indexing, and dataset management. Strong programming skills in Python and SQL, with experience in C++, Rust, or other systems programming languages. Experience with cloud-based data infrastructure, object storage, distributed computing, and workflow orchestration tools. Practical experience with data quality management, validation, monitoring, versioning, and data lineage. Familiarity with machine learning data workflows, including dataset preparation, sampling, annotation, and training data management. Demonstrated ability to lead technical initiatives, make architectural decisions, and mentor engineers.

Preferred

Qualifications Experience developing data platforms for autonomous driving, robotics, or other sensor-intensive applications. Familiarity with multimodal sensor data, including camera, LiDAR, radar, vehicle telemetry, and ROS/MCAP log formats. Hands-on experience with data exploration and visualization tools such as Foxglove, Rerun, or similar frameworks. Knowledge of embedding-based search, vector databases, active learning, or VLM-based scenario understanding. Experience with large-scale distributed data processing frameworks such as Ray, Spark, or Databricks. Familiarity with cloud infrastructure, workflow orchestration, and infrastructure-as-code tools such as Airflow, Argo, or Terraform. Experience building data annotation, automated labeling, or pseudo-labeling pipelines for machine learning. Understanding of data mining strategies, scenario coverage analysis, and dataset optimization for autonomous driving. Experience optimizing data storage, processing costs, and infrastructure performance at large scale. Track record of building data infrastructure or ML data platforms from the ground up in a research-driven or fast-growing engineering organization With respect to all your personal data shared to VinFast in the application and the entire recruitment process of VinFast, by clicking “Apply”, submitting your resumé/CV and/or participating in VinFast's recruitment process, you agree that you have read VinFast's Personal Data Protection Policy ("Policy") posted at https://vinfastauto.com/vn_vi/dieu-khoan-phap-ly or https://vinfast.vn/privacy-policy/, you agree to the Policy and consent for VinFast to process your personal data in accordance with the Policy and the applicable regulations on personal data protection. To all recruitment agencies: VinFast does not accept agency resumes. Please do not forward resumes to our careers alias or other VinFast employees. VinFast is not responsible for any fees related to unsolicited resumes.