AI Engineer

Zellerfeld Shoe Company Inc.; Hamburg; Vor Ort; EUR 90.000 - 125.000; Vollzeit

Zusammenfassung

Zellerfeld, based in Hamburg, seeks an AI Engineer to design, develop, and deploy ML solutions for cutting-edge footwear manufacturing. You will own the full ML lifecycle—from data acquisition to deployment—impactting both products and factory operations.

Join a cross-disciplinary team delivering production-grade ML systems using Python, PyTorch, TensorFlow, and cloud services. Office-based collaboration in Hamburg is required.

Qualifikationen

• Proven experience shipping ML models to production.
• Strong Python and ML stack fluency (NumPy, Pandas, scikit-learn).
• Solid ETL experience handling real-world data at scale.
• Hands-on Deep Learning with PyTorch and/or TensorFlow.

Aufgaben

• Build and maintain ETL pipelines from raw data to model-ready features.
• Design, build, and maintain ML models and pipelines for production problems.
• Own the ML lifecycle: data collection, feature engineering, training, deployment, monitoring, iteration.

Kenntnisse

Python

ML/DL engineering

PyTorch

TensorFlow

ETL pipelines

Cloud AWS/GCP

Production ML systems

NumPy/Pandas

Tools

PyTorch

TensorFlow

Docker

Kubernetes

Jobbeschreibung

Zellerfeld is revolutionising the footwear industry with the world’s first 3D-printing technology for fully customizable shoes. Our goal is simple: printed shoes on every foot. We’re a passionate team of engineers, designers, and innovators, questioning the status quo and disrupting traditional manufacturing processes. Backed by founders, influencers, and celebrities who believe in our vision, we’re committed to making a lasting, positive impact—not just on the footwear industry, but on the world.

Your Mission

You’ll design, develop, and deploy AI and machine learning solutions that push the boundaries of what's possible in footwear manufacturing. As our AI Engineer, you’ll be a key contributor to Zellerfeld’s technological advancement — building the models and pipelines that improve our products, enhance maintenance, and solve complex problems no one has tackled before. From data acquisition to model deployment, you’ll own the full ML lifecycle and help shape the intelligent systems behind the factory of tomorrow.

What You’ll Do

• Build and maintain ETL pipelines that turn raw scan, telemetry, and production data into something models can actually learn from
• Design, build, and maintain ML models and pipelines that solve real production and product problems
• Own the full lifecycle: data acquisition, feature engineering, training, deployment, monitoring, and iteration
• Integrate models into our software and production systems — they have to work in the real world, not just in notebooks
• Run experiments, evaluate honestly, and know when a model is good enough vs. when it isn’t
• Collaborate with software engineers, hardware engineers, and product teams to translate ambiguous problems into ML solutions (and to recognize when ML isn’t the right tool)
• Stay current with the field without chasing every new paper

Your profile

• Proven experience as an ML/AI Engineer, with models you've actually shipped to production
• Strong Python and fluency with the standard data/ML stack (NumPy, Pandas, scikit-learn)
• Solid ETL experience — designing and maintaining pipelines that handle real-world messy data at scale
• Hands-on deep learning experience with PyTorch and/or TensorFlow
• Solid grounding in ML fundamentals — you can explain why a model is failing, not just retrain it with different hyperparameters
• Software engineering discipline: clean code, version control, testing, and the ability to operate models you've built
• Cloud experience (AWS or GCP)
• As a company specializing in both hardware and software, we value close collaboration within our team. Therefore, we are seeking candidates who can join us at our office in Hamburg.

Nice To Have

• Computer Vision experience — directly relevant to scanning, quality control, and production
• NLP, especially applied to internal tools or customer-facing experiences
• MLOps in practice (Docker, Kubernetes, CI/CD for models, monitoring/drift detection)
• Experience working with sensor, telemetry, or manufacturing data
• Open-source contributions in the ML space