Machine Learning Researcher/Engineer (Foundational Models)

• This is an R&D position in attention-based models
• This is an extremely ambitious foundational project. There is a flexible GPU budget associated with this specific project, guaranteed to be in the 7-digit range minimum
• This role will perform (distributed) model training
• Help improve/adapt model architectures based on experiment results
• Design new tasks and experiments
• Optionally: oversee activities of team members involved in data preparation
• The results of your work will play a crucial role in the success of the project
• Preferable joining date: Immediate. The positions are open until filled – please apply immediately

Benefits

• Inclusive workplace culture
• Intellectually stimulating work environment. Be a pioneer: you get to work with a new type of data processing framework
• Responsibilities and ability to make significant contribution to the company’ success
• Work in one of the hottest data/AI startups in France, with exciting career prospects

• Have some familiarity with model monitoring, git, build systems, and CI/CD
• Have a good understanding of graph algorithms
• 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:
• We are currently searching for 1 or 2 R&D Engineers with a strong track record in machine learning models research
• 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
• You were an ICPC World Finalist, or an IOI, IMO, or IPhO medalist in High School
• You have published at least one paper at NeurIPS, ICLR, or ICML - where you were the lead author or made significant conceptual & code contributions
• Interested in improving foundational architectures and creating new benchmarks
• Fluent in English
• Respectful of others
• Have a good understanding of GPU architecture, memory design, and communication
• Experienced at hands-on experiments and model training (PyTorch, Jax, or Tensorflow)
• 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

• Cover letter: It’s always a pleasure to say hi! If you could leave us 2-3 lines, we’d really appreciate that