Machine Learning Researcher/Engineer (Foundational Models)

Join our team as a Machine Learning Researcher/Engineer, where you'll work on an ambitious foundational project in attention-based models. This R&D position offers a flexible GPU budget and the opportunity to make a significant impact on the success of the project. You'll be involved in model training, improving/adapting model architectures, designing new tasks and experiments, and potentially overseeing team members involved in data preparation. This is an exciting opportunity to work in one of the hottest data/AI startups in France with exciting career prospects.

• Participer à la formation de modèles (distribués) et à l'amélioration des architectures de modèles en fonction des résultats des expériences.
• Concevoir de nouvelles tâches et expériences, et superviser éventuellement les activités des membres de l'équipe impliqués dans la préparation des données.
• Contribuer de manière significative au succès du projet en jouant un rôle crucial dans les résultats de votre travail.
• You have spent at least 6 months working in a leading Machine Learning research center (e.g. at: Google Brain / Deepmind, Apple, Meta, Anthropic, Nvidia, MILA)
• You are expected to meet at least one of the following 4 criteria:
• Have a good understanding of graph algorithms
• You have significantly contributed to an LLM training effort which became newsworthy (topped a Huggingface benchmark, best in class model, etc.), preferably using multiple GPU's
• Interested in improving foundational architectures and creating new benchmarks
• You were an ICPC World Finalist, or an IOI, IMO, or IPhO medalist in High School
• We are currently searching for 1 or 2 R&D Engineers with a strong track record in machine learning models research
• Experienced at hands-on experiments and model training (PyTorch, Jax, or Tensorflow)
• Fluent in English
• Have some familiarity with model monitoring, git, build systems, and CI/CD
• You have published at least one paper at NeurIPS, ICLR, or ICML - where you were the lead author or made significant conceptual & code contributions
• Have a good understanding of GPU architecture, memory design, and communication
• Respectful of others
• Also, you're a deep learning researcher, with a track record in Language Models and/or RL (candidates with a Vision or Robotics ML background are also welcome to apply)
• Knowledge of approaches used in distributed training
• Familiarity with Triton
• Successful track-record in algorithms & data science contests
• Showing a code portfolio