Nil Gordillo is a data scientist and software engineer based in Barcelona with five years of experience building production ML systems and backend infrastructure for highโtraffic marketplaces and fintech startups. He has helped reconcile billions in financial flows and sped up seller payouts at Back Market, built revenue-forecasting and anomaly-detection models at Outfund, and worked on recommendation systems for millions of users at Veepee. Comfortable across the stack, he implements APIs, ETL, and model deployment using tools like FastAPI, Airflow, Kafka, Snowflake/BigQuery and PyTorch, and contributes to notable open-source projects such as LAION-AIโs OpenAssistant (backend integrations for toxicity analysis and embeddings). Combining business training with quantitative and deep learning specializations, he uniquely bridges product impact and technical rigor to turn complex data into reliable, automated workflows.
5 years of coding experience
4 years of employment as a software developer
Data Analysis - Google Cloud Professional Certificate Data Science, Data Analysis - Google Cloud Professional Certificate Data Science at Coursera
Deep Learning Artificial Intelligence, Deep Learning Artificial Intelligence at Udacity
Specialization in Front-End Development with React.js Software Engineering, Specialization in Front-End Development with React.js Software Engineering at Barcelona Activa
Data Science & Machine Learning Mathematics & Engineering, Data Science & Machine Learning Mathematics & Engineering at Universitat de Barcelona
Bachelor of Business Administration - BBA Business Development, Bachelor of Business Administration - BBA Business Development at Universitat Pompeu Fabra
OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.
Role in this project:
Back-end Developer
Contributions:23 reviews, 15 commits, 12 PRs in 12 days
Contributions summary:Nil focused on enhancing the backend functionality of the OpenAssistant project. Their contributions include implementing a Hugging Face API client for toxicity analysis, creating and integrating a new message toxicity model, and integrating message embeddings. They also addressed session management in API calls and handled the storage of toxicity scores. Additionally, the user worked on refactoring and improving the API endpoints.
Contributions:209 commits, 76 pushes, 1 branch in 1 month
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