Member of Technical Staff - Research Software Engineer

You will architect and optimize the core training infrastructure that powers our models. This includes RL training loops, distributed GPU systems, and large-scale data pipelines.

You will work closely with researchers to transform new ideas into reliable, scalable training systems.

- Designing and optimizing large-scale training loops and data pipelines.

- Implementing state-of-the-art techniques and ensuring they are numerically stable and computationally efficient.

- Building internal tooling for launching, monitoring, and reproducing complex experiments.

- Diagnosing deep bottlenecks across the training stack (GPU memory issues, communication overhead, dataloader stalls).

- Translating research prototypes into reusable, production-grade infrastructure.

- Communication optimization (NCCL, RDMA, GPU interconnects)

- You are a strong software engineer who speaks the language of machine learning.

- You may not have a PhD, but you know how to implement a research paper.

- You have deep experience in at least one of the following: Distributed Training & Inference or Data Infrastructure

- You care deeply about performance, numerical stability, and reproducibility.

- You thrive in high-agency environments and enjoy solving hard technical problems.