Member of Technical Staff - Post-training (Data Infrastructure)

Role Overview

As a Member of Technical Staff in Post-Training Data Infrastructure, you will build and own the pipelines that produce the post-training data for our large language models (LLMs), from SFT and RL data to synthetic and human-annotated data. You will work closely with researchers and annotation teams so they can iterate on data as quickly as they iterate on models.

Key Responsibilities

• Build and maintain data pipelines for SFT, preference, and RL post-training data.
• Own dataset quality, versioning, and reproducibility.
• Turn research data recipes into production-grade, reusable components.
• Scale model inference for data generation and model-based grading.
• Design data formats and schemas that keep post-training datasets consistent and efficient to train on.
• Collaborate with researchers and engineers to make sure data pipelines keep pace with the needs of cutting-edge language models.

Qualifications

• Strong software engineering skills.
• Experience designing and building large-scale data pipelines for machine learning, ideally for LLM post-training.
• Track record of running distributed data processing and model inference at scale.
• Good understanding of post-training methods (e.g., SFT, RLHF, DPO) and how data shapes model behaviour.
• Excellent communication skills.

*This is neither an exhaustive nor necessary set of attributes. Even if none of these apply to you, but you believe you will contribute to Cohere, please reach out.*