Experience

 
 
 
 
 
Machine Learning Software Engineer
May 2025 – Present Los Gatos, California (USA)
I focus on optimizing foundation models that deliver personalized recommendations for our nearly 300 million users worldwide.
 
 
 
 
 
Machine Learning Engineer
Nov 2023 – Apr 2025 Amsterdam (The Netherlands)
I deploy and maintain ML solutions across 70+ countries at the global leader in chemical distribution. Recently, I migrated Brenntag’s flagship in-house virtual AI assistant, which earns €30+ million in annual revenue, to a more cost-effective and secure AWS platform. Outside of infrastructure engineering, my primary focus is increasing ML product scalability.
 
 
 
 
 
Machine Learning Consultant
Sep 2021 – Oct 2023 Amsterdam (The Netherlands)
As a contracted ML engineer and AI developer, I optimized and implemented various ML solutions at tech startups, scale ups, and large tech companies. Notably, I spent 1 year at a leading e-classified ads platform working in an experienced team (8+ years of development experience). During my tenure, I increased the stability of the “For You” homepage recommendation system.
 
 
 
 
 
Graduate Research Intern
Jan 2020 – Aug 2020 Amsterdam (The Netherlands)
During my master’s thesis, I researched solutions to a key obstacle faced by people analytics platforms like Crunchr: encoding highly heterogeneous data (source paper). This meant encoding relational database data in a scalable way that most ML algorithms could “understand". I delivered a one-off, graph-based representation learning process. I validated my solution by using deep neural networks to classify the outputs of my process in downstream tasks.
 
 
 
 
 
Artificial Intelligence Intern
Jun 2019 – Jul 2019 Amsterdam (The Netherlands)
I established the technical foundation for automating cardiovascular disease risk prediction in a clinical research setting. According to the literature, excessive epicardial fat is associated with an increased risk in heart failure and other cardiovascular diseases. Hence, I built an end-to-end neural image segmentation pipeline to automate epicardial fat volume measurements in patient CT scans. My deliverable served as a tool for subsequent medical research.
 
 
 
 
 
Computer Vision Intern
Jun 2017 – Jul 2017 Madrid (Spain)
At Cubelizer, a Google-backed startup using computer vision for retail space optimization, I improved the accuracy of base customer detection by 12%. I developed a real-time object detection method, utilizing video processing and classical computer vision techniques. My solution fully complied with EU privacy regulations.