Generative AI ML Engineer — Seattle Hub

Atlassian; Seattle (WA); On-site; USD 147,000 - 192,000; Full time

Job summary

Atlassian, based in Seattle, is seeking a graduate ML engineer intern to build and evaluate machine learning models and Generative AI applications. You will translate business problems into technical solutions, collaborate with cross-functional teams, and contribute across the ML lifecycle from prototyping to deployment.

The role emphasizes data-driven decision making, experimentation, and integrating LLMs with tools and product context to solve multi-step problems.

Qualifications

• Currently pursuing a bachelor’s or master’s degree in Computer Science, Machine Learning, Statistics, Mathematics, Operations Research, or a related quantitative field, and graduating by August 2027
• Experience writing reliable software in Python or Scala; familiarity with SQL or Spark is a plus
• Experience applying machine learning through coursework, research, projects, or a relevant internship
• Strong problem-solving and data-driven analytical skills

Responsibilities

• Build and evaluate machine learning models and Generative AI applications
• Use data, experimentation, and feedback to improve model and product quality
• Translate customer and business problems into technical solutions with cross-functional partners
• Contribute across the machine learning lifecycle, from prototyping and evaluation to deployment and monitoring
• Develop agentic workflows that combine LLMs, tools, and relevant product context to solve complex, multi-step problems

Skills

Python

Scala

SQL

Spark

Machine learning

Analytical thinking

Education

Bachelor’s or Master’s degree in CS/ML/Stats/Math/OR or related quantitative field

Job description

Atlassian, based in Seattle, is seeking a graduate ML engineer intern to build and evaluate machine learning models and Generative AI applications. You will translate business problems into technical solutions, collaborate with cross-functional teams, and contribute across the ML lifecycle from prototyping to deployment.

The role emphasizes data-driven decision making, experimentation, and integrating LLMs with tools and product context to solve multi-step problems.