Growth ML Engineer: Personalization & Decisioning

Atlassian; Seattle (WA); Hybrid; USD 171,000 - 269,000; Full time

Benefits offered by this job

Health benefits

Volunteer days

Job summary

Atlassian is seeking a Machine Learning Engineer to join the Growth organization. You will design, build, and operate ML systems enabling personalized, measurable experiences across Atlassian’s portfolio, focusing on data foundations, models, evaluation, and cross-product recommendations.

You will partner with product, engineering, data science, analytics, marketing, and sales to translate opportunities into experiments and production capabilities, driving durable customer and business value.

Qualifications

• Bachelor's or Master's degree in computer science or equivalent experience.
• 5+ years of related industry experience in the machine learning domain.
• Proficient in Python; familiarity with Java and TypeScript; write production-grade code; SQL knowledge; Spark; cloud data environments (AWS, Databricks).
• Experience building and scaling ML models in business applications with large data
• Ability to communicate ML concepts to diverse audiences and focus on business impact.

Responsibilities

• Design, build, and operate ML systems to support Growth’s funnel and personalized experiences.
• Develop models and decisioning services for personalized recommendations and activation journeys.
• Collaborate with product, engineering, data science, analytics, marketing, and sales to translate growth opportunities into measurable experiments and production capabilities.
• Balance short-term wins with long-term strategic ML capabilities.

Skills

Python

Java

TypeScript

SQL

Spark

AWS

Databricks

Education

Bachelor's or Master's degree in Computer Science or related field

Job description

Atlassian is seeking a Machine Learning Engineer to join the Growth organization. You will design, build, and operate ML systems enabling personalized, measurable experiences across Atlassian’s portfolio, focusing on data foundations, models, evaluation, and cross-product recommendations.

You will partner with product, engineering, data science, analytics, marketing, and sales to translate opportunities into experiments and production capabilities, driving durable customer and business value.