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

D8 LooP; United States; On-site; USD 150,000 - 210,000; Full time

Job summary

Cohere is seeking a Member of Technical Staff - Post-training (Data Infrastructure) in the United States to design data formats and schemas that keep post-training datasets consistent and efficient to train on. The role focuses on owning dataset quality, versioning, and reproducibility, with responsibilities around large-scale ML data pipelines and streaming processing.

The position is part of Cohere's data platform, supporting post-training workflows and model inference at scale in an

Qualifications

• Experience designing data pipelines for ML/LLM post-training.
• Dataset versioning and reproducibility.
• Large-scale streaming data processing experience.

Responsibilities

• Design data formats and schemas to keep post-training datasets consistent and trainable.
• Own dataset quality, versioning, and reproducibility.
• Support ML data infrastructure and streaming data operations at scale.

Skills

Dataset design

Dataset quality/versioning/reproduc.

ML data pipelines

Streaming data processing

Job description

Cohere · jobs.ashbyhq.com · checked today

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

United States Staff Data Platform posted 2 d ago

Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems.

Skills, with evidence

Design data formats and schemas that keep post-training datasets consistent and efficient to train on. must have

Own dataset quality, versioning, and reproducibility. must have

• Machine learning Experience designing and building large-scale data pipelines for machine learning, ideally for LLM post-training. must have · not practised here
• Streaming Track record of running distributed data processing and model inference at scale.