Principal Machine Learning Engineer

Atlassian

23h

About The Position

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.

Requirements

Principal Machine Learning Engineer role requires driving development and implementation of ML algorithms.
Training sophisticated models is a key responsibility.
Collaboration with product, engineering, and analytics teams is essential.
Designing system and model architectures is part of the role.
Conducting rigorous experimentation and model evaluations is required.
Providing guidance to emerging ML engineers is expected.
Production-grade ML model development and deployment (ranking, retrieval, LLM-based systems) is necessary.
Feature engineering for offline training and online inference at scale is required.
End-to-end deployment of ML solutions into production, including continuous evaluation, monitoring, and improvement.
Collaboration with product managers, designers, and engineering teams to integrate AI/ML capabilities.
Partnering across engineering teams for company-wide programs.
Communicating complex technical concepts clearly to technical and non-technical stakeholders.
Mentoring and guiding junior and senior engineers.
Sharing knowledge and expertise through mentoring and coaching.
Contributing to programs of work that scale across the department.
Identifying, solving, and bridging gaps/problems across teams.
Quickly collating and analyzing key decision parameters, balancing speed, risk, and impact.
Limiting ambiguity and risk through experimentation and prototyping.
Understanding how contributions of multiple capabilities fit into larger products and platforms.

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