Member of Technical Staff - Research Software Engineer

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.

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.

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.

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.

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

• Large-scale distributed training infrastructure

• Communication optimization (NCCL, RDMA, GPU interconnects)

• FSDP / ZeRO and model sharding

Orchestration & Runtime Systems

• Ray, Kubernetes, Slurm

• Distributed runtimes and async systems

• Containerization and sandboxing

Frameworks

• PyTorch

• JAX

• Megatron-style training stacks

• Triton / custom kernels

Data Infrastructure

• Large-scale dataset curation pipelines

• Deduplication and filtering systems

• Tokenization and preprocessing

• Distributed data processing frameworks

• 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 enjoy working at the boundary between:

  • Machine learning algorithms

  • Distributed systems

  • High-performance computing

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

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