PhD AI Research Intern, 2026 Summer U.S.

Overview

Working at Atlassian
Atlassians have the flexibility to work where they can collaborate most effectively with their team, whether at home, one of our global offices, or both.

As a distributed-first company, all interviews are conducted virtually. We hire in any country where we have a legal entity. Learn how we work at go.atlassian.com/distributed.

Your Future Org

Atlassian’s Intern program combines hands-on technical training, professional growth opportunities, dedicated mentorship, and strong social connections. This holistic approach empowers students to hit the ground running and sets them up for a successful and impactful career at Atlassian!

This role is preferred to be co-located to Seattle or in the Pacific Standard Timezone.

Your Future Team

Join our cutting-edge Machine Learning Research team at Atlassian as a PhD Research Intern, where you'll have the opportunity to work on advanced machine learning (ML) and artificial intelligence (AI) technologies, particularly focusing on areas such as Large language models (LLMs), Generative AI, Conversational Agents, and AI Optimization. You’ll be part of a team that pioneers innovations to address complex real-world problems, developing state-of-the-art AI solutions that will ultimately drive impact for Enterprises.

As a PhD intern, you will gain hands-on experience in both foundational research and practical applications, contributing to the training, evaluation and optimized inferencing of AI models that could be used by millions of customers. You will collaborate closely with experienced researchers and engineers to push the boundaries of what’s possible with AI, while gaining invaluable insights into industrial research and development.

This internship is designed for individuals passionate about exploring new frontiers in AI and machine learning research. You’ll work in a stimulating environment with some of the brightest minds in the field, gaining exposure to both academic and applied research challenges. We are committed to providing a supportive and inclusive work environment where interns are empowered to contribute significantly to ongoing research that improves teamwork and collaboration in an Enterprise.

Responsibilities

Responsibilities

• Collaborate cross-functionally with Research Scientists and Machine Learning Engineers to design, implement, and evaluate experiments that advance the performance, efficiency, and scalability of modern ML and LLM systems for our AI products.

• Curate, preprocess, and manage large-scale datasets for training and evaluation, ensuring data quality, diversity, and reproducibility across experiments.

• Conduct continued training, fine-tuning, and alignment of large language models for specialized applications such as conversational AI, summarization, generative search, and multimodal agents.

• Evaluate cutting-edge ML algorithms through rigorous experimentation and provide detailed analyses highlighting performance insights, failure modes, and opportunities for improvement.

• Contribute to publications and presentations at internal workshops or top-tier academic venues, helping to drive innovation in Enterprise AI and large-scale ML systems.

Qualifications

Minimum Qualifications

• Completed Bachelors degree in Computer Science or a related field.

• Currently pursuing a PhD in Computer Science or a related field at any stage of your doctoral studies. We welcome applications from candidates in all years of their PhD program, including those in the early stages. Degree completion date cannot be earlier than September 2026 - June 2027.

• Strong foundation in AI/ML, LLMs, modeling and/or optimization techniques.

Preferred Qualifications

• Exhibit a solid grasp of algorithms and data structures.

• Demonstrate proficiency in Python programming and ability to write clean, efficient, and well-documented code.

• Experience working with large-scale datasets, including data preprocessing, augmentation, and scaling techniques.

• Has expertise in managing data using Python libraries such as NumPy, Pandas, Matplotlib, in addition to leveraging models from Hugging Face and has practical knowledge of applied machine learning and deep learning frameworks, like PyTorch.

• Demonstrated exposure to natural language processing (NLP) and Computer Vision (CV)

• Familiarity with state-of-the-art research in machine learning and AI, as evidenced by relevant coursework, publications, or