Engineering | Mountain View, United States or Remote | Remote, Remote | San Francisco, United States | Austin, United States
Working at AtlassianAtlassians 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. We are looking for a Senior Machine Learning Engineer to join our Confluence Cloud AI team.
The Confluence Cloud AI team builds and deploys machine-learning solutions that drive revenue generation, expand our active user base, and provide sophisticated forecasting for company top-line metrics. We work across the complete end-to-end machine-learning development lifecycle, from problem definition and experimentation through production deployment and ongoing improvement.
This is a unique opportunity to work in a highly collaborative environment, apply advanced machine-learning techniques, particularly forecasting models, and solve challenging, high-impact problems. You'll collaborate with stakeholders across Growth, GTM, Product, Finance, and Sales while working alongside experienced ML engineers and full-stack engineers.
Working at AtlassianAtlassians have flexibility in 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. Interviews and onboarding are conducted virtually, as part of being a distributed-first company.
- Drive the development and implementation of advanced machine-learning algorithms for business-critical applications.
- Build scalable, reliable, and highly performant forecasting algorithms, including deep-learning-based models, to forecast company top-line metrics at a granular level.
- Lead the end-to-end machine-learning development lifecycle, including problem definition, data preparation, modeling, experimentation, evaluation, deployment, and iteration.
- Design system and model architectures that support production-quality machine-learning solutions.
- Develop rigorous model-evaluation approaches and use data-driven insights to improve model quality and business impact.
- Collaborate with business, engineering, analytics, Growth, GTM, Product, Finance, and Sales teams to translate complex problems into practical solutions.
- Build and improve machine-learning infrastructure and modeling applications that can scale with the needs of the business.
- Explain machine-learning concepts and recommendations clearly to both technical and non-technical audiences.
- Provide technical guidance and mentorship to junior ML engineers.
- Balance high-quality output with business practicality, rapid iteration, and measurable customer and business outcomes.
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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