Principal Software Engineer - Confluence AI

Principal Software Engineer - Confluence AI As a Principal Software Engineer in the Confluence AI group, you will lead the development of high-quality AI experiences that help teams create, discover, and collaborate on knowledge. You will shape technical strategy across the full stack, applying advances in AI to improve product quality, deepen user trust, and drive sustained adoption and growth. Working closely with product, design, engineering, analytics, and machine learning teams, you will turn emerging technologies into reliable, intuitive capabilities that deliver lasting customer value. Technical Leadership & Execution Drive complex technical decisions that shape the direction of Confluence AI across teams and multiple quarters Lead the largest and most complex initiatives from architecture and prototyping through launch and iteration Set technical direction across user experiences, backend services, and AI integrations, balancing delivery speed, quality, and long-term maintainability Design scalable, resilient systems that meet performance, reliability, security, 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 experiences Build quality into the full product lifecycle through automated evaluations, representative test datasets, user feedback, and production monitoring Prototype and assess emerging technologies, adopting approaches that demonstrate measurable improvements in customer outcomes Partner with machine learning and platform teams to improve grounding, context retrieval, orchestration, and safeguards Establish continuous improvement processes that connect production insights to engineering priorities Product Adoption & Sustained Growth Build intuitive full stack experiences that make AI capabilities easy to discover, understand, and incorporate into everyday workflows Partner with product, design, and analytics to identify barriers to adoption and translate insights into targeted improvements Define and track measures of AI quality, activation, repeat usage, retention, and customer value Use experimentation to understand how improvements in quality and usability contribute to sustained growth Prioritize experiences that earn user trust and deliver recurring value across Confluence Cross-Functional Collaboration Collaborate closely with product managers, designers, engineers, and data scientists to deliver cohesive AI experiences Lead initiatives across Confluence and Atlassian, aligning shared capabilities with product needs Communicate technical strategy, tradeoffs, and results clearly to technical and non-technical stakeholders Bridge gaps across frontend, backend, AI infrastructure, and analytics to ensure end-to-end ownership of customer outcomes Mentorship & Organizational Impact Mentor engineers across levels, strengthening full stack engineering, AI integration, and experimentation practices Raise engineering standards through technical guidance, design reviews, and hands-on contributions Develop reusable capabilities and practices that help teams deliver high-quality AI experiences more effectively Share learning across the organization and build a culture of curiosity, accountability, and continuous improvement Decision Making & Direction Make informed decisions by balancing customer impact, technical feasibility, delivery speed, risk, and cost Reduce ambiguity through focused prototypes, experiments, and measurable success criteria Connect individual capabilities to a coherent Confluence AI strategy and broader Atlassian platform direction Anticipate evolving customer needs and technology shifts, guiding investments that support durable product quality and growth Atlassian