The team spans Platform (shared infra & clean code) and Embedded (inside research squads). Engineers can move along the research↔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; or
Embedded RE Team: Sit inside a research squad (Alignment, Pre-training, Multimodal, Safety …) and turn fresh ideas into repeatable, scalable code.

What will you do

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.

Qualifications

Master’s or PhD in Computer Science (or equivalent proven track record).
4 + years working on large-scale ML codebases.
Hands-on with PyTorch, JAX or TensorFlow; comfortable with distributed training (DeepSpeed / FSDP / SLURM / K8s).

Nice to have

Strong software-design instincts: testing, code review, CI/CD.
Self-starter, low-ego, collaborative.

Application

View listing at origin and apply!

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