Senior Software Engineer

Engineering | Seattle, United States | Remote, Remote | Canada, Canada | San Francisco, United States | Mountain View, United States or Remote



Working at Atlassian

Atlassians can choose where they work whether in an office, from home, or a combination of the two. That way, Atlassians have more control over supporting their family, personal goals, and other priorities. We can hire people in any country where we have a legal entity.

As a Senior Software Engineer in the Confluence AI group, you will build high-quality AI experiences that help teams create, discover, and collaborate on knowledge. You will work across the full stack, applying advances in AI to improve product quality, deepen user trust, and drive sustained adoption and growth. Partnering with product, design, analytics, and machine learning teams, you will turn emerging technologies into reliable, intuitive features that deliver lasting customer value.

Technical Ownership & Execution

• Own complex features from technical design and implementation through launch and ongoing improvement
• Build cohesive experiences across frontend interfaces, backend services, and AI integrations
• Contribute to architecture decisions, balancing delivery speed, quality, and long-term maintainability
• Deliver scalable, reliable software that meets performance, security, accessibility, and cost requirements

• Apply advances in generative AI, retrieval, agents, and evaluation to improve the usefulness, accuracy, and consistency of Confluence AI features
• Develop automated evaluations and representative test datasets to validate quality and prevent regressions
• Prototype and evaluate emerging technologies against concrete customer needs and measurable outcomes
• Collaborate with machine learning and platform engineers to improve grounding, context retrieval, orchestration, and safeguards
• Use production monitoring and user feedback to diagnose issues and continuously improve AI experiences

• Build intuitive experiences that make AI features easy to discover, understand, and use in everyday workflows
• Work with product, design, and analytics to identify and address barriers to adoption
• Instrument features to measure AI quality, activation, repeat usage, retention, and customer value
• Design and run experiments to assess how quality and usability improvements affect adoption and engagement
• Iterate on features to earn user trust and deliver recurring value

• Partner with product managers, designers, engineers, and data scientists to translate customer needs into practical technical solutions
• Coordinate with adjacent teams to integrate shared AI capabilities and resolve dependencies
• Communicate technical decisions, tradeoffs, and experiment results clearly
• Contribute across frontend, backend, and AI integration boundaries to deliver complete customer experiences

• Mentor engineers through pairing, code reviews, and technical guidance
• Improve team practices for testing, AI evaluation, observability, and software delivery
• Build reusable components and share approaches that help the team deliver more effectively
• Contribute to a culture of curiosity, collaboration, accountability, and continuous learning

• Independently break down ambiguous problems into achievable implementation plans
• Use prototypes, experiments, and data to reduce uncertainty and guide decisions
• Balance customer impact, technical complexity, risk, and cost when choosing solutions
• Identify opportunities for improvement and work with technical leaders to align them with the broader Confluence AI direction