Working Student ML Engineer - AI for Industrial Inspection
About the Role
At deeplify, we’re looking for a Working Student ML Engineer to join our innovative team remotely. As a Working Student ML Engineer, you will help us tackle some of the hardest applied machine learning problems in industrial inspection. This role is crucial as we build the first AI-native asset integrity co-pilot for critical industrial infrastructure, transforming how we approach maintenance decisions.
What You'll Do
- Develop deep learning models for weld defect detection and corrosion analysis using radiographic and ultrasonic data.
- Manage external labeling teams to ensure data quality and accuracy.
- Implement training, evaluation, and experiment tracking workflows to enhance model performance.
- Build production inference pipelines that integrate seamlessly into our existing systems.
- Support exciting research projects that push the boundaries of machine learning applications in industry.
Requirements
- Strong hands-on ML engineering skills with a focus on practical applications.
- High ownership and responsibility; you drive projects forward without needing constant direction.
- Ability to work quickly and efficiently, demonstrating urgency in problem-solving.
- Comfortable navigating messy, real-world data and turning it into reliable production systems.
- Bonus: Experience in computer vision, MLOps, production ML, imaging, or sensor data.
Nice to Have
- Familiarity with Python and popular ML libraries such as TensorFlow or PyTorch.
- Understanding of data pipelines and cloud infrastructure.
- Experience with version control systems like Git.
What We Offer
- Work on technically ambitious problems that have a real industrial impact.
- Build end-to-end ML systems, contributing to projects from conception to deployment.
- Be part of a team addressing long-term challenges like corrosion prediction.
- Enjoy well above-average compensation for working students.
- Gain valuable experience in a fast-paced, innovative environment.
This role offers a unique opportunity for students to gain hands-on experience in machine learning while working on impactful projects in the industrial sector.
Who Will Succeed Here
Proficiency in Python and experience with libraries such as TensorFlow or PyTorch for building and deploying deep learning models, ensuring the candidate is not only familiar with code but can also apply it effectively in real-world scenarios.
Self-motivated and comfortable working independently in a remote setting, demonstrating strong time management skills to meet project deadlines while balancing academic responsibilities.
Hands-on experience with MLOps practices and tools like Docker and Kubernetes to streamline the deployment and monitoring of machine learning models, showcasing a proactive mindset towards operationalizing ML solutions.
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