AI Engineer at Zellerfeld Shoe Company Inc. / Zellerfeld R&D GmbH

Hamburg, , Germany -

Full Time

Start Date

Immediate

Expiry Date

31 Aug, 26

Salary

0.0

Posted On

02 Jun, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow, ETL Pipelines, Deep Learning, AWS, GCP, Computer Vision, NLP, MLOps, Docker, Kubernetes, CI/CD

Industry

Retail Apparel and Fashion

Description

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) 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 Why us? At Zellerfeld, you will find a multidisciplinary team of engineers, SW and HW specialists, entrepreneurs, designers, and students with a common passion. We embrace contrarian thinking, humility, pushing limits, and improving ourselves. We utilize a flat hierarchy, and we want your voice to be heard. We also care about bonding because we want to be a tight-knit community, so be prepared to spend quality time with everyone, including the CEO! 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. About us Zellerfeld is revolutionizing 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.

Responsibilities

Design, develop, and deploy AI and machine learning solutions to optimize footwear manufacturing and product maintenance. This includes owning the full ML lifecycle from data acquisition and ETL pipeline construction to model deployment and monitoring.