Senior Machine Learning Engineer

We're Creative Fabrica, a fast-scaling tech start-up from Amsterdam.

Our mission

Enable creativity by giving access to everything related to the creative process: inspiration, learning, creating, and sharing. The ‘Creative Inspiration Flywheel’

This creates a self-sustaining community that lives within the Creative Fabrica ecosystem.

About the platform

We launched in 2016 as a marketplace for fonts with a subscription service. In the last 8 years, we have been through tremendous growth, and we're just getting started!

We have scaled to 50 million+ monthly page views, 8 million+ registrations, over 22 million listed products, and 22000+ active designers.

Our focus is to create a platform for designers that enables them to offer exceptional high-quality content. We make their products searchable in unique ways. This allows our customers to speed up their workflow, allowing them to focus on what they love most: Creating.

About the team:

Our AI team is building tools that help crafters generate designs seamlessly and exactly as they envision them. We work with cutting-edge text-to-image and image-to-image models, personalizing outputs based on user preferences and needs. By leveraging millions of craft designs and state-of-the-art hardware, we’re training models to push the boundaries of craft generation. We also integrate computer vision and computer graphics to ensure the highest quality results for our users.

About the role:

We're looking for an experienced Senior Machine Learning Engineer specializing in personalization systems to help transform how crafters discover and engage with Creative Fabrica's content. This role focuses on building sophisticated recommendation engines, search optimization, and ranking systems that drive user engagement and top-of-funnel conversions. You'll leverage our rich catalog of millions of craft designs and user behavioral data to create intelligent systems that connect crafters with exactly what they need for their creative projects. 

While our core focus is on recommendation systems and personalization, familiarity with or enthusiasm for generative AI models (particularly image generation) is a strong plus. This is especially relevant as we integrate these technologies into our Studio experiences, empowering crafters to create custom designs from their imaginations. You'll work at the intersection of classical ML recommendation techniques and cutting-edge generative models to build the most engaging and relevant creative discovery platform overall.

What you will do:

• Design and implement personalization systems including retrieval, recommendation engines, and ranking algorithms that leverage our rich catalog of millions of craft designs and user behavioral data to drive engagement and discovery in our marketplace.
• Own the end-to-end lifecycle of machine learning models, from data analysis and feature engineering to training, deployment, monitoring, and iteration, ensuring robust and scalable solutions in production.
• Lead data analysis and feature engineering initiatives to extract meaningful signals from user interactions, content attributes, and behavioral patterns, creating robust feature stores that power our recommendation engines.
• Collaborate with product, growth, and engineering teams to integrate recommendation outputs into user experiences, design and execute A/B tests to measure recommendation system impact, and iterate on models based on experimental results, business metrics, and user engagement data.
• Continuously monitor, test, and optimize deployed models to ensure reliability, scalability, and efficiency in production environments.
• Work on cutting-edge generative models for image generation to give crafters the best AI-assisted crafting experience in our Studio application by brainstorming, prototyping, and integrating generative capabilities as part of the AI team.

What you will need to have:

• Proven experience in building personalization engines, with a strong understanding of recommendation models, semantic search, and ranking algorithms, using both classical ML and modern deep learning approaches.
• Strong expertise in feature engineering and feature stores, with experience extracting signals from user behavioral data, content attributes, and interaction patterns to power ML models at scale.
• Experience in scalable model serving frameworks and distributed task queues for real-time recommendation and ranking systems.
• Proven track record in A/B testing and experimentation frameworks for measuring ML model impact, with experience in statistical analysis and causal inference methodologies.
• Strong Python expertise for machine learning with deep knowledge of recommendation system libraries (scikit-learn, TensorFlow, PyTorch) and experience building high-performing data pipelines in distributed environments.
• Familiarity with computer vision techniques for applications like visual search and product similarity, enabling the system to recommend items based on visual attributes.
• Knowledge of cloud platforms (AWS, GCP, or Azure), MLOps practices, CI/CD pipelines, and automated testing for production ML systems.

It would be great if you could also bring:

• Experience with productizing vision models
• Hands-on experience with text-to-image and image-to-image models, including fine-tuning, prompt engineering, and model optimization techniques.