Fluency in Python plus one systems language (C++, Rust, Go or Java)
Hands‑on with container orchestration and schedulers (Kubernetes / K8s, SLURM, or similar)
Comfortable profiling performance, optimising I/O, and automating workflows
Self‑starter, low-ego, collaborative, high‑energy
(Desirable) Exposure to ML workloads or data-processing pipelines
(Desirable) Experience with GPU clusters or CUDA
(Desirable) Open‑source contributions or widely used internal tools
What the job involves
As a Research Engineer on the software side, you will design and harden the codebase, tools and distributed services that let our scientists train and ship frontier‑scale models. You do not need prior ML experience; what matters is writing clean, reliable code that scales
You will join our Platform REs team to build and maintain shared dev‑tools, evaluation & data pipelines, training framework, cluster tooling and CI/CD
Accelerate researchers by owning the complex parts of large-scale pipelines and delivering robust internal tooling
Interface research with product: expose clean APIs, automate model pushes, surface live metrics
Write efficient, well‑tested Python and systems code; enforce code review, CI, and observability
Design and optimise distributed services (Kubernetes / SLURM, thousands‑of‑GPU jobs)
Prototype utilities (CLI, dashboards) and carry them through to stable, shared libraries
Based in Paris and London, our REs move fluidly along the research and production spectrum. Engineers can rotate between Platform and Embedded tracks as their interests evolve
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