SonarSource
Software Engineer - Agentic Code Review (Gitar)
About this role
Sonar is seeking a Software Engineer to develop agentic code review capabilities that verify and govern AI-generated code across enterprise platforms. You'll build tools that integrate with leading AI coding assistants to ensure software reliability, security, and compliance at scale.
What you'll do
- Design and implement agentic code review features within SonarQube and related products
- Develop integrations with AI coding assistants (Claude Code, Copilot, Cursor, Devin, etc.)
- Build scalable systems for code verification, governance, and repair automation
- Collaborate with product and ML teams to enhance agent effectiveness and explainability
- Contribute to SonarQube Foundation Agent and context augmentation capabilities
- Write clean, maintainable code while adhering to Sonar's quality standards
What they're looking for
- Software engineering and system design
- Agent-based systems and AI/ML concepts
- API integration and microservices architecture
- Code analysis or static analysis tools experience
- Cloud platforms and DevOps practices
- Python, Java, or similar backend languages
- Git and version control workflows
- Enterprise software development
Benefits
- Work on cutting-edge AI and code verification technology
- Collaborate with teams across 8 global hubs
- Impact customers including Nvidia, Goldman Sachs, and Booking.com
- Profitable, fast-growing company with $400M+ revenue
- Opportunity to shape the future of agent-centric development
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SonarSource
SonarSource builds AI-powered code review and verification solutions that help enterprises ensure software quality, security, and governance. The company is hiring cloud infrastructure engineers, technical writers, sales engineers, and solutions specialists to support its platform and serve federal and enterprise customers.
- Website
- sonarsource.com
Likely interview questions
- What experience do you have working with AI-assisted development tools or agentic systems?
- How would you approach designing a code review system that works across multiple AI coding assistants?