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:
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Platform RE Team: Enhance the shared training framework, data pipelines and cluster tooling used by every team; or
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Embedded RE Team: Sit inside a research squad (Alignment, Pre-training, Multimodal, …) and turn fresh ideas into repeatable, scalable code.
What you'll 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.
What you need
• 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).
• Experience in deep learning, NLP or LLMs; bonus for CUDA or data-pipeline chops.
• Strong software-design instincts: testing, code review, CI/CD.
• Self-starter, low-ego, collaborative.
