Principal Machine Learning Engineer

Atlassian is a company focused on unleashing the potential of every team through their software products. As a Principal Machine Learning Engineer, you will lead the development and implementation of advanced machine learning algorithms and collaborate with various teams to integrate AI functionalities into their products.

Responsibilities:

• 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

Requirements:

• 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