Principal Machine Learning Systems Engineer - Search & Intelligence

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



Atlassian is a global leader in team collaboration and productivity software, empowering teams to unleash their potential and achieve remarkable results. Our suite of tools, including Jira, Confluence, Trello, Bitbucket, and Rovo, is trusted by millions of users worldwide to plan, track, and manage their work effectively. At Atlassian, we are dedicated to building innovative tools and technologies that drive the future of work.

We are looking for a Principal Machine Learning Engineer to join our Search & Intelligence organization. The team builds Atlassian's flagship AI products, including agentic search and chat, long-horizon agents, and capabilities that enrich Atlassian's Teamwork Graph. We also develop the frameworks, models, and infrastructure that accelerate AI feature development across the company.

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.

What you'll do

As a Principal Machine Learning Engineer, you will lead the development and implementation of cutting-edge machine learning algorithms and sophisticated models that power AI capabilities across Atlassian's products and services. You will collaborate closely with product, engineering, and analytics teams to translate complex business and customer needs into impactful solutions.

Your responsibilities will span designing system and model architectures, leading rigorous experimentation and evaluation, and providing technical mentorship to emerging ML engineers. Beyond these core responsibilities, you will help shape our AI strategy and ensure its transformative potential is realized across Atlassian.

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.