Senior Principal Machine Learning Engineer - LLM Post-Training and Optimization

Atlassian is a global leader in team collaboration and productivity software, empowering teams to unleash their potential and achieve remarkable results. Our suite of tools, including Jira, Confluence, Trello, Bitbucket, and Rovo, is trusted by millions of users worldwide to plan, track, and manage their work effectively. At Atlassian, we are dedicated to building innovative tools and technologies that drive the future of work. We're seeking a highly skilled and experienced Senior Principle Machine Learning Engineer to propel our efforts in Large Language Model (LLM) post-training and optimization, shaping the future of intelligent, team-centric solutions.
Your Role and Impact:

• LLM Post-Training and Optimization: Spearhead the optimization and enhancement of cutting-edge large language models (LLMs) to deliver superior performance and efficiency. Your work will span fine-tuning, optimization, and deployment of advanced LLMs tailored to a variety of applications.
• Advanced Data Analysis: Dive deep into vast datasets to extract meaningful insights. Your work will power the AI models to understand complex user interactions and content within Atlassian tools to propel team work forward across the globe.
• Collaborative Innovation: You will work closely with cross-functional teams, including researchers, engineers, designers, engineering and product managers, to ensure our models deliver best-in-class performance, efficiency, and scalability.
• AI Evangelism: Be the voice of AI within the team. Educate and inspire your colleagues about the potential of AI, leading workshops and knowledge-sharing sessions.

• Lead the fine-tuning and post-training optimization of large language models (LLMs) for diverse applications.
• Develop and implement techniques for model compression, quantization, pruning, and knowledge distillation to optimize performance and reduce computational costs.
• Conduct research on advanced techniques in transfer learning, reinforcement learning, and prompt engineering for LLMs.
• Design and execute rigorous benchmarking and evaluation frameworks to assess model performance across multiple dimensions.
• Collaborate with infrastructure teams to optimize LLM deployment pipelines, ensuring scalability and efficiency in production environments.
• Stay at the forefront of advancements in LLM technologies, sharing insights, driving innovation within the team, and leading agile development.
• Mentoring other team members, facilitating within/across team workshops, fostering a culture of technical excellence and continuous learning.

• Ph.D. or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
• 8+ years of experience in machine learning, with a focus on large-scale model development and optimization.
• Deep expertise in LLM and transformer architectures (e.g., GPT, BERT, T5).
• Strong proficiency in Python and ML frameworks such as PyTorch, JAX, or TensorFlow.
• Experience with distributed training techniques and large-scale data processing pipelines.
• Proven track record of deploying machine learning models in production environments.
• Familiarity with model optimization techniques, including quantization, pruning, and knowledge distillation.
• Strong problem-solving skills and ability to work in a fast-paced, collaborative environment.
• Excellent communication skills and ability to translate technical concepts for diverse audiences.

Preferred Qualifications:

• Experience with multi-modal LLMs or domain-specific fine-tuning.
• Knowledge of cloud-based ML platforms (e.g., AWS, GCP, Azure).
• Contributions to open-source ML projects or publications in top-tier conferences.
• Familiarity with MLOps practices and tools.