Autonomous Driving Software Engineer - Datasets & MLOps
Summary
Machine learning engineer owning datasets and MLOps for the perception stack of ADASTEC's SAE Level-4 automated driving platform. Day to day: preparing and versioning multi-sensor (camera, LiDAR, radar) driving datasets, building auto-labeling and quality-check workflows, and supporting reproducible training pipelines using Python, AWS S3, Docker, and PyTorch.
ADASTEC delivers SAE Level-4 Automated Driving Software Platform for commercial vehicles to enable OEMs to develop modern, automated, shared, and connected commercial vehicles. ADASTEC flowride.ai SAE Level-4 Automated Driving Software Platform integrated buses are on public roads in 14 countries in Europe & North America.
flowride.ai is designed with the fusion of different high-precision sensors available in the global market and complies with safety standards and regulations.
ADASTEC has an HQ and operation office in Michigan, USA, an EU Operations office in the Netherlands, Germany, and an R&D Office in Türkiye. For more information, visit www.adastec.com
We are hiring an Autonomous Driving Software Engineer who work as a Machine Learning Engineer to own datasets and MLOps for our perception stack. You will work with perception engineers, annotators, and model owners to turn multi-sensor driving data into reliable training sets, and to keep those sets versioned, measurable, and ready for automated labeling and training.
RESPONSIBILITIES
- Preparing, organizing, and maintaining multi-modal datasets (camera, LiDAR, radar)
- Building and operating dataset versioning, storage, and access for training jobs
- Defining annotation requirements and quality checks with perception and annotationteams
- Developing auto-labeling workflows and tools that validate dataset and label quality
- Monitoring dataset coverage and domain shift, and work with model owners when data is the issue
- Supporting automated training pipelines with stable, reproducible data inputs
QUALIFICATIONS
- Bachelor’s or master’s in Computer Science, AI, Electrical/Electronics Engineering, Robotics, or a related field
- Minimum 2 years of professional experience as a Machine Learning Engineer
- Excellent command of English, both written and spoken
- Having strong Python knowledge; comfortable writing tested data tooling
- Having experience in preparing and managing datasets for computer vision or 3D perception
- Working knowledge of cameras, LiDAR, or radar
- Having experience with object storage (AWS S3 or compatible)
- Understanding of annotation workflows and annotation quality
- Proficiency in Linux, Git, Docker
PREFERED QUALIFICATIONS
- Dataset versioning (lakeFS, DVC, or similar)
- Annotation platforms (CVAT, Labelbox, Supervisely, Segments.ai, or similar)
- Multi-sensor calibration and time synchronization
- AWS (S3, IAM, EC2) and containerized GPU environments
- Auto-labeling or human-in-the-loop review workflows
- Familiarity with domain shift and dataset coverage analysis
- Enough PyTorch to understand what training jobs need from data