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**