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

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