Research Software Engineer

Our Mission

Reflection is a research lab making intelligence open and accessible for everyone to use, customize, and build on. We build open models that let anyone control their intelligence and help shape the future of AI. Our mission: make intelligence open and accessible to all.

The Roles Mission

Bridge the gap between research and production by turning cutting-edge algorithms into scalable training systems. You will design and optimize the core infrastructure behind frontier AI models — from reinforcement learning training loops and distributed GPU training to massive-scale data pipelines.

Our systems train models across thousands of GPUs and process petabyte-scale datasets. We care deeply about numerical stability, throughput, and reproducibility.

What This Team Does

This team owns and evolves the core infrastructure behind our training systems.

We focus on:

• Reinforcement learning training infrastructure

• Distributed training and inference systems

• Experiment infrastructure and reproducibility

• Large-scale data pipelines

The goal is to build the engineering foundation that allows researchers to iterate quickly while training models at massive scale.

About the Role

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.

Responsibilities include:

• 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.

What You'll Work With

Distributed Training

• GPU parallelism (data, tensor, pipeline, expert)