Software Engineer, Bugbot
Full Stack Engineer · Mid · Full Time
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Role
What you'll do.
Cursor is seeking a full-stack Software Engineer to work on Bugbot, an AI-powered code review assistant that helps engineering teams ship higher-quality software. This role involves building end-to-end features, integrating LLMs into the development workflow, and ensuring the product maintains exceptional quality and reliability for millions of engineers.
Responsibilities
- End-to-end feature development: Launch new Bugbot features from concept to production, including UI design, backend implementation, and model behavior optimization
- Review pipeline evolution: Adapt prompting strategies, model routing, context selection, and agent orchestration to leverage new AI capabilities
- Integration development: Build seamless integrations that embed Bugbot into engineering workflows and development processes
- Quality assurance and reliability: Monitor precision and recall metrics, triage false positives, and ensure consistent, trustworthy AI review output
- User onboarding design: Create adoption flows that transition teams from installation to daily active usage of Bugbot
- Cross-team collaboration: Partner with ML, infrastructure, and product teams to drive model improvements and scale system architecture
- Product surface ownership: Own Bugbot's complete user experience including features, integrations, configuration, and AI code review interface
- Performance optimization: Manage cost and latency while maintaining high-quality AI review capabilities
- Customer feedback integration: Collect and implement feedback from both internal and external users to improve product quality
Qualifications
What we look for.
Technical
Full-stack development experience
Proven ability to ship product features end-to-end across frontend, backend, and system integration layers
AI/ML integration experience
Experience building or working with agents that integrate into code review or CI/CD workflows
Product development lifecycle
Experience launching products or features to external users with full ownership of onboarding and iteration
Developer tooling background
Understanding of developer experience and familiarity with code review processes and workflows
Education
Computer Science degree
Bachelor's degree in Computer Science, Software Engineering, or related technical field (or equivalent experience)
Technical portfolio
Strong portfolio demonstrating full-stack development capabilities and AI integration experience
Experience
Full-stack product shipping
Demonstrated experience moving fluidly between frontend, backend, and model integration layers
Customer-facing product launch
Experience with external product launches including documentation, feedback collection, and iterative improvement
Quality-focused development
Ability to balance rapid iteration with maintaining product trust and reliability
Collaborative development
Experience working with cross-functional teams including ML, infrastructure, and product specialists
Skills
Required
Full-stack development
Proficiency in both frontend and backend technologies with ability to ship complete features
AI/ML integration
Experience integrating large language models and AI agents into production applications
Developer tooling
Deep understanding of developer workflows, code review processes, and CI/CD integration
Product quality focus
Obsessive attention to quality with experience in evaluation metrics and user feedback integration
System design
Ability to design scalable systems that handle model routing, context selection, and agent orchestration
Preferred
Code review automation
Nice to havePrevious experience building or maintaining automated code review tools or systems
Developer experience optimization
Nice to haveTrack record of improving developer productivity through tooling and workflow enhancements
ML model evaluation
Nice to haveExperience with precision/recall metrics and evaluation frameworks for AI systems
API integration
Nice to haveFamiliarity with GitHub, GitLab, or other version control platform APIs
Performance optimization
Nice to haveExperience optimizing cost and latency in AI-powered applications
Tech stack
Languages
Frameworks
Databases
Tools
Other
Process
Interview steps.
- 01
Initial screening
Phone or video call with recruiting team to discuss background and role fit
- 02
Technical phone screen
45-minute technical interview focusing on full-stack development and system design concepts
- 03
Take-home project
Real-world coding challenge involving AI integration or developer tooling (3-4 hours)
- 04
Technical deep-dive
Review of take-home project with focus on architecture decisions and code quality
- 05
Product and culture fit
Discussion with team members about product vision, collaboration style, and company values
- 06
Final interview
Meeting with senior leadership to discuss career goals and long-term vision alignment
Full posting
Original listing.
Our mission is to automate coding. The first step in our journey is to build the best tool for professional programmers, using a combination of inventive research, design, and engineering. Our organization is very flat, and our team is small and talent dense. We particularly like people who are truth-seeking, passionate, and creative. We enjoy spirited debate, crazy ideas, and shipping code.
About the role
We're hiring a Software Engineer to work on Bugbot. Bugbot reviews pull requests, catches bugs, suggests improvements, and is becoming a critical part of how engineering teams ship software. As LLMs rapidly mature, the surface area of what Bugbot can do is expanding just as fast.
From the model integration and agent harness improvement to the product UI, integrations, and onboarding experience, you’ll maintain Bugbot being the industry's best AI code reviewer. This is a full-stack IC role where you'll ship features end-to-end and directly shape how millions of engineers interact with AI during code review.
What you’ll do
Launch new Bugbot features end-to-end from defining the feature and building the UI to wiring up the backend and iterating on model behavior so that each release meaningfully improves code review quality. You can check out Bugbot blog post for some examples of how Bugbot was improved in the past: https://cursor.com/blog/building-bugbot .
Evolve the review pipeline adapting prompting strategies, model routing, context selection, and agent orchestration to take advantage of new capabilities while managing cost and latency.
Build integrations that embed Bugbot into every engineer's workflow making AI-assisted review a seamless, default part of the development loop.
Own product quality and reliability monitor precision and recall, triage false positives, improve observability across the review pipeline, and ensure Bugbot earns trust with every review it posts.
Design onboarding and adoption flows that help teams go from first install to daily active usage.
Partner with ML, infrastructure, and product teams to inform model improvements with real-world review data, shape the Bugbot roadmap, and scale the system as adoption grows.
You will own Bugbot's product surface end-to-end: features, integrations, review pipeline, onboarding, configuration, and the user experience of AI code review.
You will not own foundation model training or core infrastructure services — but you will be a key consumer and collaborator, driving requirements based on what Bugbot needs.
You will not be a backend-only engineer or an ML researcher who doesn't ship product. This role requires you to move fluidly between the model layer and the product layer.
Quality is the product. A code review assistant that posts noisy or unhelpful comments is worse than no assistant at all. You'll be obsessive about making Bugbot's output genuinely useful. You obsess about your evals.
You may be a fit if
You’ve built or worked on agents that integrate into the code review or CI/CD workflow.
You’ve shipped full-stack product features end-to-end and enjoy moving between frontend, backend, and model integration.
You care deeply about developer experience and collecting new evals from customers and internal team members.
You can hold the tension between "ship fast to learn" and "don't erode trust with bad suggestions."
You’ve launched a product or feature to external users and owned the full loop: onboarding, documentation, feedback, and iteration.
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