Atlassian
Machine Learning Intern, 2027 Summer U.S.
San Francisco, United States +1 · Internship · Posted Oct 28, 2026
- Location
- San Francisco, United States
Seattle, United States - Education
- Currently enrolled full-time in a Master’s degree program in a quantitative field (Computer Science, Machine Learning, AI, Data Science, Statistics, Mathematics, or related field); returning to the program after the internship and graduating by August 2028.
- Visa sponsorship
- Not eligible for F1 and J1 students; no work sponsorship.
About the job
Build and evaluate AI/ML solutions for product and business problems, contributing to focused projects across applied ML, Generative AI, and ML platforms.
What you'll do
- Build and evaluate applied AI/ML solutions, including models, agents, retrieval, and evaluation workflows.
- Translate business and product needs into technical projects, from experimentation and prototyping to implementation.
- Improve solutions through experimentation and communicate results, trade-offs, limitations, and next steps.
What they ask for
- Currently enrolled full-time in a quantitative Master’s degree program, returning after the internship and graduating by August 2028.
- Strong software engineering fundamentals, including experience in Python and familiarity with testing, version control, APIs, data structures, and debugging.
- Hands-on experience building machine learning or AI systems through coursework, research, or industry internships.
- Able to commit to a 12-week, full-time (40 hours/week) Summer 2027 internship.
Pay
Zone A: $54/hr - $61/hr USD base salary; Zone B: $49/hr - $55/hr USD base salary; Zone C: $45/hr - $51/hr USD base salary
Tools
Python, Generative AI, recommendation/ranking, NLP, classification, embeddings, LLM applications, RAG
AI focus
Develop applied AI/ML and Generative AI solutions, including models, agents, retrieval, recommendation and optimization systems, governance and compliance intelligence, and ML platforms; evaluate outcomes and use AI-assisted tools thoughtfully.
Model-written summary · the original posting is authoritative