Cognition
Coding AI · Devin autonomous coding agent
Currently tracking 13 active AI roles, down 27% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $260k–$300k (avg $280k).
Coding AI · Devin autonomous coding agent
Cognition currently has 22 active AI-related job listings, with a significant focus on roles within the "agents" stage, accounting for 64% of their openings. The majority of these positions are in Engineering. The company is actively hiring for roles related to agent orchestration, LLM observability, and tool use.
Cognition currently has 24 active AI-related roles in our index. The most common open titles are: Partner Deployed Engineer - APAC (2), AI Enablement Engineer, AI Support Engineer, Applied AI Engineer - ANZ, Applied AI Engineer - APAC. Most positions are in Engineering and Product.
Cognition's active AI hiring is concentrated in: agents (67%), application (21%), serving infrastructure (4%). 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 (13 roles), United Kingdom (2 roles), Singapore (2 roles), Japan (2 roles).
Job postings at Cognition most frequently mention: CI/CD, System Design, Kubernetes, Tool-Using Agents, Technical Leadership.
In the past 30 days, Cognition has posted 5 new AI-related roles.
| Title | Stage | AI score |
|---|---|---|
| General Application Applied AI lab building end-to-end software agents, starting with Devin (AI software engineer) and Windsurf (AI-native IDE), aiming to create collaborative AI teammates for complex real-world tasks. | Agent | 10 |
| Product Engineer Product Engineer at an applied AI lab building end-to-end software agents, specifically focusing on the Devin AI software engineer and Windsurf AI-native IDE. The role involves owning features end-to-end, building performant and beautiful experiences across IDE, web, and CLI surfaces, setting the standard for agent UX, and working directly on the core agent infrastructure including the agent loop and tool use. | Agent | 9 |
| Software Engineer |
| Agent |
| 9 |
| 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 |
| 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 |
| Software Engineer, Infrastructure Software Engineer, Infrastructure role at an applied AI lab building end-to-end software agents (like Devin, the AI software engineer). The role focuses on building and operating the core infrastructure for agent execution, including compute, orchestration, networking, and platform systems. This involves owning agent execution infrastructure (sandboxed compute environments, VM orchestration, container management), building the developer platform (CI/CD, deployment systems), driving reliability and observability, and scaling infrastructure with product growth. Requires deep systems engineering, cloud/container expertise (Kubernetes, AWS/GCP/Azure), strong Python skills, observability instincts, and a security/isolation mindset for agentic workloads. | AgentServe | 8 |
| Partner Deployed Engineer This role focuses on enabling Global Systems Integrators (GSIs) to successfully deploy, scale, and deliver value with Cognition's AI agents (Devin and Windsurf) to their client base. The Partner Deployed Engineer will work closely with partners to build repeatable offerings, provide technical enablement, co-deliver pilots, and act as a technical representative within partner organizations, influencing the product roadmap based on field learnings. | Agent | 8 |
| Deployed Engineer, Federal Applied AI lab building end-to-end software agents, specifically Devin (AI software engineer) and Windsurf (AI-native IDE). This role is for a foundational engineer in the federal group, focusing on deploying and integrating these agents within federal enterprise accounts, translating customer needs into product requirements, and architecting complex deployments. | Ship | 7 |
| AI Enablement Engineer This role focuses on enabling enterprise engineering teams to adopt and integrate AI software agents (like Devin) into their workflows. The engineer will lead workshops, guide customers, pair-program, identify high-ROI workflows, create enablement materials, and collaborate on scaling AI Enablement into a productized offering. It requires strong software engineering fundamentals, customer-facing experience, and the ability to teach and scale learnings. | Agent | 7 |