Research Engineer (Machine Learning)

• The team spans Platform (shared infra & clean code) and Embedded (inside research squads). Engineers can move along the research‑to‑production spectrum as needs or interests evolve.
• As a Research Engineer – ML track, you’ll build and optimise the large‑scale learning systems that power our open‑weight models. Working hand‑in‑hand with Research Scientists, you’ll either join:
• Platform RE Team: Enhance the shared training framework, data pipelines and cluster tooling used by every team;
• Embedded RE Team: Sit inside a research squad (Alignment, Pre‑training, Multimodal, …) and turn fresh ideas into repeatable, scalable code.
• Accelerate researchers by taking on the heavy parts of large‑scale ML pipelines and building robust tools.
• Interface cutting‑edge research with production: integrate checkpoints, streamline evaluation, and expose APIs.
• Conduct experiments on the latest deep‑learning techniques (sparsified 70 B+ runs, distributed training on thousands of GPUs).
• Design, implement and benchmark ML algorithms; write clear, efficient code in Python.
• Deliver prototypes that become production‑grade components for Le Chat and our enterprise API.

Benefits

• Competitive bonus structure
• Equity
• Opportunities for professional growth and development

• Hands‑on with Py

  Torch, JAX or Tensor

  Flow; comfortable with distributed training (Deep

  Speed / FSDP / SLURM / K8s).
• Strong software‑design instincts: testing, code review, CI/CD.
• 4 + years working on large‑scale ML codebases.
• Self‑starter, low‑ego, collaborative.
• Master’s or PhD in Computer Science (or equivalent proven track record).
• Experience in deep learning, NLP or LLMs; bonus for CUDA or data‑pipeline chops.