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Principal Software Engineer - Confluence AI

Job in Winnipeg, Manitoba, Canada
Listing for: Atlassian
Full Time position
Listed on 2026-10-17
Job specializations:
  • Software Development
    AI Engineer (Applied/Software), Full Stack Developer, Software Architect
Salary/Wage Range or Industry Benchmark: 209700 CAD Yearly CAD 209700.00 YEAR
Job Description & How to Apply Below
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 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

Compensation
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.

Please visit  for more information on which locations are included in each of our geographic pay zones. However, please confirm the zone for your specific location with your recruiter.

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