Principal Machine Learning Engineer

As a Principal Machine Learning Engineer, you will drive the development and implementation of cutting-edge machine learning algorithms, training sophisticated models, and collaborating with product, engineering, and analytics teams to build AI functionalities into Atlassian products and services. Your daily responsibilities will encompass a broad spectrum of tasks designing system and model architectures, conducting rigorous experimentation and model evaluations, and providing guidance to emerging ML engineers. Your role is pivotal, ensuring AI's transformative potential is realized across our offerings.

Technical Leadership & Execution

• Drive complex decisions that impact the work of teams and change their technical direction over multiple quarters
• Regularly tackle the largest and most complex problems on the team, from technical design to launch
• Set the direction of systems and capabilities, balancing progress over perfection
• Determine plans-of-attack on large projects and solve complex architecture challenges

• Design, develop, and deploy production-grade ML models (e.g., ranking, retrieval, LLM-based systems) to optimize user experience and achieve business objectives
• Conduct meticulous experimentation and model evaluations, backing decisions with data
• Develop robust feature engineering practices to ingest, process, and serve features for offline training and online inference at scale
• Oversee end-to-end deployment of ML solutions into production, ensuring continuous evaluation, monitoring, and improvement

• Collaborate closely with product managers, designers, and engineering teams to integrate AI/ML capabilities into products
• Partner across engineering teams to take on company-wide programs spanning multiple projects
• Communicate complex technical concepts clearly to both technical and non-technical stakeholders

• Mentor and guide junior and senior engineers, fostering a culture of innovation, collaboration, and continuous learning
• Actively share knowledge and expertise through mentoring and coaching beyond direct reports
• Contribute to programs of work that scale across the department
• Identify, solve, and bridge gaps/problems across teams using experience and expertise

• Quickly collate and analyze key decision parameters, balancing speed, risk, and impact appropriately
• Limit ambiguity and risk by experimenting and prototyping
• Understand how contributions of multiple capabilities fit into larger products and platforms