Cognition is actively hiring for 18 AI-related roles. The majority of these positions, 61%, are focused on agents, with application roles making up another 22%. Engineering is the most frequent function for these hires. Over the last 30 days, Cognition has posted 5 new AI roles, representing a 400% increase compared to the previous 30-day period.
Currently tracking 14 active AI roles, up 55% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $260k–$300k (avg $280k).
Cognition currently has 18 active AI-related roles in our index. The most common open titles are: Partner Deployed Engineer - APAC (2), AI Enablement Engineer, Applied AI Transformation Manager - APAC, Applied AI Transformation Manager - Europe , Data Engineer, Core Product. Most positions are in Engineering and Product.
Cognition's active AI hiring is concentrated in: agents (61%), application (22%), serving infrastructure (6%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Cognition is hiring AI talent in: United States (12 roles), United Kingdom (2 roles), India (2 roles), Singapore (1 role).
Job postings at Cognition most frequently reference: agent orchestration, agent research, evals, tool use, code gen.
In the past 30 days, Cognition has posted 2 new AI-related roles. That is a -60% change versus the prior 30 days (5 → 2).
| Title | Stage | AI score |
|---|---|---|
| Research, Post-Training Research role focused on post-training and alignment of AI agents, shaping their behavior and capabilities for real-world usefulness and safety. Blends research and engineering to iterate on datasets, training stages, hyperparameters, and evaluation design, with a focus on understanding and advancing techniques like RLHF/RLAIF for long-horizon tasks. | Post-trainAgent | 9 |
| Research, Mid-Training This role focuses on the critical mid-training stage of LLMs, bridging pre-training and post-training. The goal is to sharpen raw base model capabilities in areas like reasoning, generalization, coding, and math through strategic data mix, quality uplift, annealing schedules, context length extension, and synthetic data generation. The role involves both research and engineering, with a strong emphasis on evaluation and iteration to ensure measurable capability gains for AI agents like Devin. | PretrainPost-train | 9 |