Principal Machine Learning Engineer

Working at Atlassian

Atlassians can choose where they work – whether in an office, from home, or a combination of the two. That way, Atlassians have more control over supporting their family, personal goals, and other priorities. We can hire people in any country where we have a legal entity. Interviews and onboarding are conducted virtually, a part of being a distributed-first company.

• Regularly tackle the largest and most complex problems in the team, from technical design to launch.
• Work closely with Product, Engineering and Design leads in Jira AI, and translate their requirements into solid engineering deliverables, delegating work to the teams.
• Deliver solutions that are used by other teams and products.
• Follow a Product Engineer mindset by building features that are data-driven and customer-centric, fostering that culture within the Jira AI group.
• Exceptional problem solving ability using ML, AI and core software engineering. Routinely tackle complex architecture challenges and define architectural standards.
• Actively contribute to the code delivery through leading code reviews & documentation, direct contribution and fixing complex bugs in high-risk surface areas.
• Expertise in data analysis, statistical methods, and logical reasoning to inform data-driven decision-making.
• Partner across engineering teams to take on company-wide initiatives spanning multiple projects.
• Mentor junior members on the team.

On your first day, we’ll expect you to have:

• Fluency in at least one modern object-oriented programming language (preferably Java/Kotlin and Python).
• Understanding of Machine Learning project lifecycle/tools along with prompt engineering.
• Experience in architecting and implementing high-performance RESTful microservices.
• Experience building and operating large scale distributed systems using Amazon Web Services (S3, Kinesis, Cloud Formation, EKS, AWS Security and Networking).
• Experience with leveraging LLMs effectively and optimizing model usage on GPUs.
• Experience with Databricks or Apache Spark.
• Experience with Continuous Delivery and Continuous Integration.

It would be great, but not required:

• Expert-level SQL knowledge, query tuning, schema design, and ETL processes.
• Experience with Amazon Sagemaker.
• Experience with scaling and deploying Machine Learning models.