ML Data Engineer

About the role

This role owns the data foundation of perception systems, directly influencing model performance in real-world environments. You will architect and manage the end-to-end ML data lifecycle, from ingestion and pipeline architecture to labeling strategy and continuous dataset improvement. Working as a senior technical partner to ML and robotics teams, you will transform raw sensor data into production-grade systems at scale.

Responsibilities

• Architect and own scalable pipelines for ingesting, organizing, and preprocessing large volumes of time-series camera and multi-sensor data.
• Drive the strategy behind 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, including tooling decisions, QA methodology, and consistency standards.
• Partner with AI Engineers to diagnose model weaknesses and translate findings into concrete dataset strategy.
• Plan and lead data collection campaigns to close gaps with high-value real-world data.
• Build internal tools and dashboards that provide visibility into dataset quality and performance gaps.

Required skills

• Python
• Pandas
• NumPy
• ETL/ELT
• Data Orchestration
• Object Detection
• CVAT
• Sensor Data
• Data Lifecycle Management
• SQL
• NoSQL

Nice to have

• AWS S3
• MinIO
• ClearML
• MLFlow
• Weights & Biases
• ROS
• Docker
• Embedded ML
• LiDAR
• Radar

Qualifications

• 5+ years of hands-on experience in Python and data processing frameworks
• Proven track record building and owning ETL/ELT pipelines for large-scale video and sensor datasets
• NATO-aligned nationality or close ally citizenship

Benefits

• International, agile team environment
• Continuous development
• Team events

About the Company

autonomous-teaming is an engineering team focused on creating the future of autonomous systems and AI-driven defense.