PlusAI is a Physical AI company pioneering AI-based virtual driver software for factory-built autonomous trucks. Headquartered in Silicon Valley with operations in the United States and Europe, Plus was named by Fast Company as one of the World’s Most Innovative Companies. Partners including TRATON GROUP’s Scania, MAN, and International brands, Hyundai Motor Company, Iveco Group, Bosch, and DSV are working with Plus to accelerate the deployment of next-generation autonomous trucks. If you’re ready to make a huge impact and drive the future of autonomy, Plus is looking for talented individuals to join its fast-growing teams.
This role will work on a project to design and implement a fast columnar file format for storing data collected during autonomous vehicle operations. The file format should be optimized for fast reading of individual topics, compression, and compatibility with object storage. The format should be designed in a way to support large-scale data processing via Spark or similar.
If possible and time allows, implement compatibility with ROS2. In addition to the file format, design and build tooling for reading and writing, so that the format can be integrated into Plus data processing.
Responsibilities:
Explore and evaluate candidate file formats based on targeted use cases, benchmark their performance against key workload requirements
Integrate the selected format into the existing data processing workflow.
Required Skills:
Strong programming skills in C++ and/or Python, with the ability to design, implement, test, and debug production-quality software.
Solid understanding of data structures, algorithms, operating systems, and file I/O performance.
Experience or strong interest in large-scale data systems, storage formats, distributed processing, or data-intensive applications.
Basic understanding of compression, serialization, indexing, and random/sequential read performance trade-offs.
Preferred Skills:
Familiarity with columnar data formats or storage concepts, such as Parquet, Arrow, ORC, HDF5, MCAP, or similar formats.