Cohere
AI Frontier · Enterprise LLMs
AI Frontier · Enterprise LLMs
Cohere has 77 active AI-related job listings. The majority of these roles are focused on agents, representing 39% of the total. Engineering is the dominant function, with 60 positions. The company is actively hiring for roles related to model serving, agent orchestration, and fine-tuning. In the last 30 days, Cohere has posted 17 new AI roles, a significant increase compared to the previous 30-day period.
Currently tracking 69 active AI roles, down 44% versus the prior 4 weeks. Primary focus: Agent · Engineering.
Cohere currently has 83 active AI-related roles in our index. The most common open titles are: Forward Deployed Engineer, Agentic Platform (2), Solutions Architect - Public Sector (2), Applied AI Engineer - Agentic Workflows (Singapore), Applied AI Engineer – Agentic Workflows, Applied AI Engineer – Agentic Workflows (Korea). Most positions are in Engineering and Research.
Cohere's active AI hiring is concentrated in: agents (36%), data (20%), serving infrastructure (17%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Cohere is hiring AI talent in: Canada (37 roles), United States (15 roles), United Kingdom (15 roles), France (3 roles).
Job postings at Cohere most frequently reference: model serving, agent orchestration, fine tuning, rag, inference infra.
In the past 30 days, Cohere has posted 12 new AI-related roles. That is a -33% change versus the prior 30 days (18 → 12).
| Title | Stage | AI score |
|---|---|---|
| Member of Technical Staff, Data Analysis and Evaluation Cohere is seeking a Member of Technical Staff, Data Analysis and Evaluation, to ensure the quality, reliability, and performance of their LLMs. The role involves designing data collection tasks, evaluating dataset quality, analyzing model robustness and generalizability, and collaborating with cross-functional teams. Responsibilities include overseeing data collection, developing statistical methods for dataset evaluation, analyzing ML system generalizability, improving dataset quality and model performance, training LLMs, and conducting experiments. | DataPost-train | 8 |