[Remote] Principal Machine Learning Engineer
Note: The job is a remote job and is open to candidates in USA. 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
Skills
- 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
Benefits
- Health and wellbeing resources
- Paid volunteer days
Company Overview
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