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

Autonomous Teaming Solutions; München; On-site; EUR 90,000 - 130,000; Full time

Job summary

Autonomous Teaming Solutions in Munich seeks a senior ML data engineer to own the data foundation of perception systems, turning raw sensor data into reliable production-grade datasets for AI and robotics.

You will shape ingestion, labeling, QA, and dataset improvement, collaborating with ML, perception, and robotics teams to boost model performance in real-world environments.

Qualifications

• 5+ years hands-on experience in Python and data processing frameworks (Pandas, NumPy).
• Proven track record building ETL/ELT pipelines for large-scale video and sensor datasets.
• Experience with data lifecycle management for ML/CV workflows, including dataset versioning and reproducibility.
• Strong command of object detection pipelines (Detectron2, MMDetection, COCO format).
• Experience designing active learning, uncertainty sampling, or semi-supervised dataset workflows.
• Familiarity with data annotation platforms (CVAT, Label Studio) and automated QA/consistency checks.
• Strong grasp of evaluation metrics for object detection (IoU, mAP, precision-recall).
• Comfortable owning decisions around databases and large-scale dataset management.
• Track record of cross-functional collaboration with perception, deployment, robotics, and data infra teams.
• Fluent in English; German and/or French are a plus.

Responsibilities

• Architect and own scalable pipelines for ingesting, organizing, and preprocessing large volumes of time-series data.
• Drive the strategy behind object detection datasets for quality and representativeness at scale.
• Design and operate active learning loops that connect model performance to data selection.
• Own labeling workflows end-to-end, tooling decisions, QA methodology, and annotation coordination.
• Partner with AI engineers to diagnose weaknesses and translate findings into dataset strategy.
• Plan and lead data collection campaigns to capture high-value real-world data.
• Build internal tools and dashboards to monitor dataset quality and coverage.

Skills

Python

Pandas

NumPy

Data processing

ETL/ELT pipelines

Active learning

Object detection datasets

Labeling QA

Cross-functional collaboration

Tools

Detectron2

MMDetection

COCO format

CVAT

Label Studio

SQL/NoSQL

ROS

Job description

What we offer

• Work in an international, agile team creating the future of autonomous systems
• Grow your career in a expanding and ambitious engineering team
• Build innovative products using state-of-the-art technologies in AI, robotics, and autonomy
• Benefit from a steep learning curve and continuous development
• Enjoy team events and a strong, collaborative culture

Your mission

This role owns the data foundation of our perception systems end-to-end — the layer that directly determines model performance in real-world environments. You'll set the technical direction for how we collect, curate, and continuously improve the datasets behind object detection, working as a senior technical partner to ML, perception, and robotics teams — turning raw, messy sensor data into reliable, production-grade systems at scale.

You will take full ownership of the ML data lifecycle — from architecture decisions on ingestion and pipelines, through labeling strategy and QA, to driving continuous, metrics-informed dataset improvement — and will be expected to bring judgment and prior experience to how this is done, not just execute a defined process.

What you'll do:

• Architect and own scalable pipelines for ingesting, organizing, and preprocessing large volumes of time-series camera and multi-sensor data (RGB, IR, thermal, depth, IMU)
• Drive the strategy behind our object detection datasets, ensuring quality, diversity, and statistical representativeness at scale
• Design and operate active learning loops that connect model performance directly to data selection and improvement priorities
• Own labeling workflows end-to-end — tooling decisions, QA methodology, consistency standards, and coordination of annotation efforts
• Partner closely with AI Engineers to diagnose model weaknesses, bias, and drift, and translate findings into concrete dataset strategy
• Plan and lead data collection campaigns (field recordings, drone/video capture) to close gaps with high-value real-world data
• Build internal tools and dashboards that give the org visibility into dataset quality, distribution, and performance gaps

Your profile

• 5+ years of hands-on experience in Python and data processing frameworks (Pandas, NumPy, vectorized operations, multiprocessing)
• Proven track record building and owning ETL/ELT pipelines for large-scale video and sensor datasets in production
• Deep experience with data orchestration and lifecycle management for ML/computer vision workflows, including dataset versioning and reproducibility
• Strong command of object detection pipelines (Detectron2, MMDetection, COCO format, bounding-box standards)
• Demonstrated experience designing active learning, uncertainty sampling, or semi-supervised dataset workflows
• Deep familiarity with data annotation platforms (CVAT, Label Studio) and building automated QA/consistency checks
• Strong grasp of evaluation metrics for object detection (IoU, mAP, precision-recall curves, class-wise metrics)
• Comfortable owning decisions around databases (SQL/NoSQL), file systems, and large-scale image, video, and sensor dataset management
• Track record of working cross-functionally and influencing perception, deployment, robotics, and data infrastructure teams
• Fluent in English; German and/or French are a plus

Nice to have

• Experience with cloud storage and MLOps tools (AWS S3, MinIO, ClearML, MLFlow, Weights & Biases).
• Familiarity with ROS / robotics data formats (bag files, TF trees, sensor_msgs), Docker, or embedded ML workflows.
• Prior work with robotics, drones, or multi-sensor perception systems, including IR, LiDAR, radar, or audio datasets.

What else

• Outside-the-box creativity with a blend of conceptual and systematic design thinking.
• High intrinsic motivation, attention to detail, and strong problem-solving mindset.
• Structured, methodical, and reliable execution, even under uncertainty.
• Humble, collaborative, and mission-driven — values collective success over ego.
• High ethical standards and disciplined work ethic.
• Extra-curricular achievements, leadership, or unique projects are a plus.
• NATO-aligned nationality or close ally citizenship is required.

Why us?

Join us to shape the future of AI-driven defense!

Keywords

Active Learning, Machine Learning, Python, Pandas, Data Orchestration, CVAT, NumPy, Data Engineering, Data Pipelines, Dataset Engineering, Data Processing, Data Lifecycle, ELT / ETL, Sensor Data, Video Data Processing