Rémy Frenoy

Dust - Staff Engineer

Jan. 2026 - present

• Dust is an AI platform that lets companies build and deploy AI agents connected to their internal knowledge — Slack, Notion, Google Drive, GitHub and more — to amplify every team's work.
• Leveraged knowledge: LLM integration, retrieval-augmented generation (RAG), TypeScript/React, Next.js.

Sorare - Staff Engineer

Oct. 2022 - Dec. 2025

• Sorare is a global fantasy sports platform (Football, NBA, MLB) where users scout real players, build lineups with digital cards, and win rewards based on real-world performances.
• Implemented fraud detection solutions, reducing fraud by more than 80%, implemented core game elements (Sorare crafts) and improved the marketplace (recommendations). Built the data analytics platform from scratch with Manuel Pozo.
• Leveraged knowledge: Ruby on Rails, GraphQL, PostgreSQL, Redis, dbt, Snowflake.

Jellysmack - Lead AI

Nov. 2021 - Oct. 2022

• Jellysmack is the global creator company that detects and develops the world's most talented video creators through technology.
• Leading a team of 10 data scientists, we make products that help video editors.
• Leveraged knowledge: Image/video processing, Computer Vision.

Octo Technology - Senior Machine Learning Engineer

Feb. 2019 - Nov. 2021

• In addition to my work as an engineer, I am supervising and mentoring teams of data scientists and ML engineers to be up to date with state of the art solutions and apply software development best practices. I have worked on projects using the most common cloud service providers such as AWS, GCP or Azure. Examples of projects: Creating a predictive maintenance software for a major actor in the energy industry, creating a software that detects bacteria from pictures of petri dishes for an actor of the pharmaceutical industry.
• Leveraged knowledge: Same as below, plus Knowledge sharing, Cloud environments (AWS, GCP, Azure).
• Open source contribution: psquare, implementation of The p² algorithm for dynamic calculation of quantiles and histograms without storing observations by Jain and Chlamtac.

Octo Technology - Machine Learning Engineer

Feb. 2017 - Nov. 2021

• Help tackling business issues with software and data (ML involved or not).
• Working on ways to improve our development/deployment tools and methods to make it easier/faster/safer to deploy efficient machine learning and data pipelines in a production environment.
• I am also a trainer for the Advanced Data Science courses (levels I and II) and Apache Spark courses given by Octo Academy.
• Leveraged knowledge: Python programming (pandas, pyspark, numpy/scipy, scikit-learn, tensorflow, pytorch, unittest/pytest, setuptools), Airflow, Jenkins/Gitlab CI. Exploratory Data Analysis. Supervised learning (mostly ensemble models on tabular data, time series). Model selection. Object-Oriented and Functional programming. Agile organization.

University of Technology of Compiègne - PhD Student

Oct. 2013 - Oct. 2016

• Topic: User modeling and adaptation of action selection in interactive training environments
  • Error recognition: Studied and used supervised learning models in order to recognize patterns of error from users gestures (supervised multilabel classification).
  • User modeling: Studied and developed a framework where users are represented in a multidimensional metric space, making it possible to extract a personalized learning path and monitor the evolution of users' profiles over time.
  • Adaptation: From user modeling, adaptation of action selection can be seen as a multi-armed bandit problem. Proposed and implemented a contextual multi-armed bandit algorithm to make a system automatically and dynamically adapt to the user over time. This work has been published as a full paper at the ACM User Modeling, Adaptation and Personalization Conference (ACM UMAP 2016).
• Leveraged knowledge: Supervised learning, Bandit algorithms, Python programming, Version control (git), Technical writing.
• Publications:
  • Adaptive Training Environment without Prior Knowledge: Modeling Feedback Selection as a Multi-armed Bandit Problem
  • Contributions of mixed reality in a calligraphy learning task: Effects of supplementary visual feedback and expertise on cognitive load, user experience and gestural performance
  • (In French) Modélisation de l'activité gestuelle et sélection automatique de feedback pour des environnements interactifs d'apprentissage : application à la calligraphie
• Teaching activities:
  • Artificial Intelligence course (IA04): teaching assistant.
  • Introduction to virtual reality (RV01): teaching assistant.
  • Participated in the creation of the practice exercises (french content).

Praxedo - Software Engineer

Feb. 2013 - Oct. 2013

• Built prototypes based on several NoSQL solutions (especially MongoDB) to measure their efficiency/scalability within the context of Praxedo data. Proposed a new version of MongoDB data balancer to optimize update actions on sharded clusters.
• Leveraged knowledge: Programming in Java/Javascript, knowledge of advantages and limitations of different kinds of data storage paradigms, deploying/monitoring/optimizing MongoDB reading/writing operations under various situations.
• Publication: Design and Implementation of a MongoDB solution on a Software As a Service Platform

University of Technology of Compiègne - PhD, Computer Science

Oct. 2013 - Oct. 2016

Linköping University - Master's, Computer Science (double degree)

Aug. 2012 - Jun. 2013

Coursework: Distributed Computing, Database systems

University of Technology of Compiègne - Engineering Degree, Computer Science

Sept. 2008 - Jun. 2013

Coursework: Knowledge Representation, Algorithms and data structures, Operating Systems, Networks