# ML Data Engineer (m/f/d) - Sensor Data & Pipelines at Autonomous Teaming

> Germany · Unspecified · ML / AI · open. Source: https://alderhq.app/jobs/personio%3Aautonomous-teaming%3A2799678 (read 2026-11-09).

## Facts (observed)

- Company: [Autonomous Teaming](https://alderhq.app/companies/autonomous-teaming)
- Location text: Munich (DEU)
- Where it can be done from (inferred): Germany (evidence: "Location: Munich (DEU)")
- Posted: 2026-09-16 (54 days ago)
- First seen by alder: 2026-11-04 · last verified: 2026-11-07
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://autonomous-teaming.jobs.personio.de/job/2799678
- Skills listed (inferred): Python, AWS, Docker, SQL

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (Germany, n 5): p25 €68k, median €85k, p75 €175k

## Description (the employer's text)

### 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

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!

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/personio%3Aautonomous-teaming%3A2799678 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.