ML Data Engineer (m/f/d) - Sensor Data & Pipelines

Autonomous-teaming in Munich is seeking a Senior Level ML Data Engineer (m/f/d) - Sensor Data & Pipelines to join its IT team. In this senior role, you will own the data foundation of perception systems end-to-end, setting technical direction for how datasets are collected, curated, and improved for object detection in autonomous systems.

Core Responsibilities

• Architect and own scalable ingestion and preprocessing pipelines for large-scale camera and multi-sensor time-series data, including RGB, IR, thermal, depth, and IMU.
• Drive the dataset strategy for object detection to ensure statistical representativeness, quality, and diversity at scale.
• Design active learning loops connecting model performance directly to data selection priorities.
• Manage labeling workflows end-to-end, covering tooling decisions, QA methodology, and consistency standards.
• Partner with AI Engineers to diagnose model weaknesses, drift, and bias, and plan field data collection campaigns.
• Build internal tools and dashboards to monitor dataset quality, distribution, and performance gaps.

Requirements and Background

Candidates must hold a Bachelor’s degree and bring 5+ years of hands-on experience in Python and data processing frameworks such as Pandas, NumPy, and multiprocessing. You need a proven track record of building production ETL/ELT pipelines for large-scale video and sensor datasets, along with deep experience in data orchestration and lifecycle management for computer vision workflows.

Applicants should also possess a strong command of object detection pipelines like Detectron2, MMDetection, and COCO formats, as well as experience designing active learning or semi-supervised workflows. Familiarity with annotation platforms such as CVAT and Label Studio, strong knowledge of object detection evaluation metrics (IoU, mAP, precision-recall), and database management skills (SQL/NoSQL) are required. Fluency in English is mandatory, while German and/or French are considered a plus. Cloud storage and MLOps tools, ROS or robotics data formats, and prior work with drones or multi-sensor perception systems are nice-to-haves.

Autonomous-teaming offers market competitive compensation and a collaborative environment within an expanding engineering team working on AI, robotics, and autonomy.