Cognition
Software Engineer
About this role
Join an applied AI lab building AI software engineers and IDE tools. You'll tackle core infrastructure challenges for agentic systems that power Devin and Windsurf, working on long-horizon task execution, tool use, and developer experience at the frontier of AI.
What you'll do
- Design and ship agent infrastructure including tool use, context management, planning, and code execution environments
- Develop editor intelligence and agent-in-the-loop workflows for Windsurf IDE
- Translate new model capabilities into shipped features through collaboration with researchers
- Build reliable, performant systems handling millions of agentic tasks at scale
- Contribute to real-time code understanding and developer experience improvements
- Shape product direction and define what AI software engineering looks like
What they're looking for
- Distributed systems design and reliability engineering
- Python proficiency with production codebase ownership
- Systems thinking and failure mode analysis
- Product intuition and developer experience design
- LLM and agent architecture understanding
- Fast iteration and decision-making under ambiguity
- High code quality standards
- IDE or developer tools experience
Benefits
- Base salary $260,000–$300,000 plus significant early-stage equity
- Fully paid medical, dental, and vision coverage for employee and dependents
- 401(k) with company match
- Private chef, snacks, and workplace perks
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Cognition
Cognition builds AI software engineers and developer tools, including Devin (an AI software engineer) and Windsurf (an AI-native IDE) that help developers automate tasks and write code more efficiently. The company is hiring Deployed Engineers to work directly with customers on adoption and integration, SREs to manage production reliability and infrastructure, federal engineers for government deployments, and IT specialists to support internal operations.
View all jobs at CognitionLikely interview questions
- Walk us through a complex distributed system you've built in production. How did you approach reliability and failure modes, and what would you do differently?
- Describe your experience with Python at scale. Tell us about a large Python codebase you've owned and how you maintained code quality while shipping fast.