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Atlassian

Principal Software Engineer - Confluence AI

Posted 45 Minutes Ago
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In-Office or Remote
Hiring Remotely in IN, USA
Expert/Leader
In-Office or Remote
Hiring Remotely in IN, USA
Expert/Leader
Lead technical strategy and development of Confluence AI experiences across frontend, backend, and AI integrations. Design scalable systems, apply generative AI, retrieval, agents, evaluation, and safeguards, and improve product quality, adoption, trust, and customer value. Drive complex initiatives from architecture through launch, partner cross-functionally, mentor engineers, establish quality practices, and guide experimentation and continuous improvement across Atlassian.
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Engineering | Canada, Canada | Remote, Remote | San Francisco, United States | Seattle, 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 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

At Atlassian, we strive to design equitable, explainable, and competitive compensation programs. We follow consistent hiring practices and account for each candidate's skills, knowledge, and experience when setting base pay within the range.
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Atlassian New York, New York, USA Office

Atlassian believes the future of work is distributed and offers our people the flexibility to help them do what’s important to them. And with few exceptions, we hire people anywhere we have a legal entity as long as they have eligible work rights and sufficient team time zone overlap.

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