Cohere is hiring a Member of Technical Staff Post training Data Infrastructure in Toronto!
Cohere, an enterprise-focused AI company building foundation models, is hiring a Member of Technical Staff to own the data infrastructure behind post-training for its large language models. The role sits at the junction of research and engineering: you would design and operate the systems that generate, grade, store, and version the supervised fine-tuning, preference, and reinforcement-learning data that shapes model behaviour. Much of the work involves translating informal research recipes into dependable, reusable production components, and scaling inference so that synthetic generation and model-based grading keep up with experiment velocity. The position is based in Toronto within a globally distributed company, and the posting emphasizes remote-friendliness alongside physical offices and a co-working benefit. Compensation is not disclosed in the listing.
Key responsibilities
- Build and maintain pipelines that produce SFT, preference, and reinforcement-learning post-training data, including synthetic and human-annotated sources
- Own dataset quality, versioning, and reproducibility across post-training datasets
- Convert 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
- Partner with researchers, engineers, and annotation teams so data iteration keeps pace with model iteration
Key requirements
- Strong software engineering fundamentals
- Experience designing and building large-scale data pipelines for machine learning, ideally in the LLM post-training domain
- Track record of running distributed data processing and model inference at scale
- Working understanding of post-training methods such as SFT, RLHF, and DPO, and how data choices shape model behaviour
- Excellent communication skills for cross-functional collaboration with researchers and annotation teams
- Seniority: Member of Technical Staff (experienced individual contributor)
- Work mode: Remote-friendly; Toronto office available
- Key skills: Large-scale data pipelines, Distributed data processing, Model inference at scale, LLM post-training (SFT, RLHF, DPO), Dataset versioning and reproducibility, Data schema design, Software engineering
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