Data Engineer - Mobile Mapping & AI Infrastructure (m/f/d)

Meyer Talent Acquisition; Berlin; Vor Ort; EUR 85.000 - 120.000; Vollzeit

Benefits dieser Stelle

Massive geospatial data scale

No-legacy MLops approach

Async-first culture

Vienna office anchor day

Zusammenfassung

Meyer Talent Acquisition seeks a Data Engineer for Mobile Mapping & AI Infrastructure. You will architect cloud-native data systems ingesting petabytes of geospatial data from cameras and LiDAR, preparing it for CV/ML models and producing production-ready datasets.

You’ll design end-to-end data pipelines, manage scalable AKS clusters, and collaborate with ML researchers to industrialize experimentation into robust, repeatable workflows using Flyte and Terraform.

Qualifikationen

• Experience designing and operating data pipelines for CV/ML.
• Strong Python data manipulation and cloud-native tooling.
• Familiarity with geospatial data formats and workflows.

Aufgaben

• Design and manage multi-stage data workflows using Flyte to produce ML-ready datasets.
• Maintain and scale processing clusters on AKS for GPU/CPU tasks.
• Build CV data pipelines with data versioning and quality gates.
• Handle large-scale LiDAR and imagery storage with low-latency access.
• Collaborate with ML researchers to production-ize experiments.
• Develop resilient pipelines and CI/CD for data workloads.

Kenntnisse

Python scripting

Geospatial data handling

ML/CV workflows

Tools

Flyte

Azure Kubernetes Service (AKS)

Terraform

Helm

Kubernetes

Jobbeschreibung

Data Engineer – Mobile Mapping & AI Infrastructure (m/f/d)

The Role

As a Data Engineer you'll join one of our clients through Meyer Talent Acquisition. You are the architect of the "data refinery." The 360-degree cameras and LiDAR sensors generate petabytes of raw street-level data every year. Your mission is to build the robust, cloud-native infrastructure that ingests, processes, and prepares this massive geospatial volume for our Computer Vision (CV) and Machine Learning models. You will bridge the gap between raw sensor acquisition and production-ready AI, ensuring that our "Digital Twin" of the world is updated with pinpoint accuracy and maximum efficiency.

Key Responsibilities

• Orchestration & Pipelines: Design and manage complex, multi-stage data workflows using Flyte to automate the transition from raw sensor data to ML- ready datasets.
• Scalable Infrastructure: Maintain and scale processing clusters on Azure Kubernetes Service (AKS), ensuring high availability and cost-efficient resource allocation for GPU/CPU-heavy tasks.
• CV Data Engineering: Build specialized pipelines for Computer Vision, including automated data versioning, quality gates, and Active Learning loops.
• Geospatial Data Lifecycle: Manage large-scale storage and retrieval patterns for LiDAR and imagery, optimizing data formats for low-latency access by both humans and algorithms.
• MLOps Collaboration: Work alongside ML Researchers to industrialize research code, turning experimental Python scripts into hardened, reproducible production workflows. Technical Requirements
• Data Orchestration: Extensive experience with Flyte (or Airflow/Prefect), building resilient, versioned, and reproducible pipelines.
• Cloud & Containers: Deep proficiency in Azure and Kubernetes - Helm charts, K8s operators, cloud-native storage.
• Python Mastery: Expert-level Python, particularly for high-performance data manipulation (Pandas, NumPy) and interfacing with CV libraries.
• Data Architecture: Strong understanding of modern data stack patterns (Lakehouse/Mesh) and experience handling unstructured data (images, point clouds, video).
• DevOps Mindset: Infrastructure as Code (Terraform) and CI/CD pipelines experience.

What We Offer

• Massive Scale: Work with one of the largest specialised geospatial datasets in the world (180+ million panoramic images yearly).
• Cutting-Edge Stack: A "no-legacy" approach to MLOps - utilising the best modern tools to solve problems others haven't encountered yet.
• Flexible working: Async-first culture. Vienna office anchor day Wednesday.
• English working language - German not required.