Working Student ML Engineer

<p>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.</p>
<h2>Tasks</h2>
<p>We are looking for an <strong>exceptional ML engineer</strong> 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.</p>
<p>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.</p>
<ul>
<li>Deep learning models for weld defect detection and corrosion analysis on radiographic and ultrasonic data</li>
<li>Managing external labeling teams</li>
<li>Training, evaluation, and experiment tracking workflows</li>
<li>Production inference pipelines</li>
<li>Support an exciting research project</li>
</ul>
<h2>Requirements</h2>
<ul>
<li>Strong hands-on ML engineering skills</li>
<li><strong>High ownership</strong>: you take responsibility, drive things forward, and do not wait to be told every next step</li>
<li><strong>High urgency</strong>: you move fast, care about execution, and know how to create momentum</li>
<li>Excited by messy, difficult, real-world problems with no obvious solution</li>
<li>Comfortable working across data, models, infrastructure, and deployment</li>
<li>Bonus: experience in computer vision, MLOps, production ML, imaging, or sensor data</li>
</ul>
<h2>Benefits</h2>
<ul>
<li>Work on technically ambitious problems with real industrial impact</li>
<li>Build end-to-end ML systems, not just models in isolation</li>
<li>Help lay the foundation for a scalable internal ML platform</li>
<li>Be part of a team tackling long-term challenges like corrosion prediction, a genuinely hard problem with significant upside</li>
<li><strong>Well above average working student compensation</strong></li>
</ul>
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