- Location
- Germany
- Workplace
- Hybrid
- Type
- part-time
- Department
- Entry level
- Seniority
- Internship
- Industry
- Industrial Technology
- Category
- Engineering
- Role type
- Individual Contributor
- Environment
- Office
- Source
- JOIN
Overview
Du arbeitest an anspruchsvollen Machine Learning-Problemen in der industriellen Prüfung – von Schweißnahtfehlererkennung bis Korrosionsanalyse. Deine Aufgaben umfassen das Training von Deep Learning-Modellen, die Verwaltung von Labeling-Teams, Aufbau von Produktions-Pipelines und Unterstützung von Forschungsprojekten. Erforderlich sind fundierte ML-Engineering-Fähigkeiten, Eigenverantwortung und Erfahrung in Computer Vision oder MLOps ist von Vorteil.
Description
At deeplify, we’re building the first AI-native asset integrity co-pilot for critical industrial infrastructure. We turn inspection data from pipelines, chemical plants, ships, and bridges into real-time, risk-based maintenance decisions. We combine a digital inspection platform with proprietary deep-learning models and an evolving agentic AI system that learns from asset integrity engineers. This shifts asset integrity from slow, analogue, document-driven processes to a proactive, software-defined, and increasingly autonomous system.
## Tasks
We are looking for an **exceptional ML engineer** working student to help us solve some of the hardest applied machine learning problems in industrial inspection — from weld defect detection and corrosion analysis on radiographic data to future UT-based systems and long-term corrosion prediction.
This is not a narrow research role. It is about solving hard end-to-end real-world problems: turning messy industrial data into reliable production systems.
- Deep learning models for weld defect detection and corrosion analysis on radiographic and ultrasonic data
- Managing external labeling teams
- Training, evaluation, and experiment tracking workflows
- Production inference pipelines
- Support an exciting research project
## Requirements
- Strong hands-on ML engineering skills
- **High ownership**: you take responsibility, drive things forward, and do not wait to be told every next step
- **High urgency**: you move fast, care about execution, and know how to create momentum
- Excited by messy, difficult, real-world problems with no obvious solution
- Comfortable working across data, models, infrastructure, and deployment
- Bonus: experience in computer vision, MLOps, production ML, imaging, or sensor data
## Benefits
- Work on technically ambitious problems with real industrial impact
- Build end-to-end ML systems, not just models in isolation
- Help lay the foundation for a scalable internal ML platform
- Be part of a team tackling long-term challenges like corrosion prediction, a genuinely hard problem with significant upside
- **Well above average working student compensation**
Skills
Benefits
Languages
About deeplify
deeplify entwickelt den ersten KI-basierten Co-Piloten für die Asset-Integrität kritischer industrieller Infrastrukturen.
