Senior Software Engineer

Overview

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.

Responsibilities

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

AI Quality & Innovation

• 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

Product Adoption & Sustained Growth

• 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

Cross-Functional Collaboration

• Partner with product managers, designers, engineers, and data scientists to translate customer needs into practical technical solutions