Cognition
Coding AI · Devin autonomous coding agent
Coding AI · Devin autonomous coding agent
Currently tracking 14 active AI roles, up 55% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $260k–$300k (avg $280k).
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.
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 Engineer, Infrastructure Research Engineer, Infrastructure role at Cognition, an applied AI lab building end-to-end software agents like Devin. The role focuses on building and owning the core systems that researchers depend on, including distributed training infrastructure, experiment orchestration, data pipelines, and tooling to accelerate research velocity. This involves ensuring systems are fast, reliable, and scalable for large-scale training jobs across thousands of GPUs, with a focus on performance optimization and parallelism strategies. The ideal candidate has deep experience in distributed systems, Python/C++, PyTorch, GPU profiling, and ML knowledge to engage with researchers. | Serve | 9 |