Senior Machine Learning Engineer

Atlassian•Austin, TX

6d•$171,063 - $269,075•Remote

About The Position

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. Atlassians 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.

Requirements

Advanced machine-learning algorithms
Forecasting algorithms
Deep-learning-based models
End-to-end machine-learning development lifecycle
Problem definition
Data preparation
Modeling
Experimentation
Evaluation
Deployment
Iteration
System and model architectures
Production-quality machine-learning solutions
Model-evaluation approaches
Data-driven insights
Machine-learning infrastructure
Modeling applications
Technical guidance
Mentorship

Responsibilities

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.