Senior ML System 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. As a Senior ML System Engineer in the Rovo GenAI Platform team, you will build and maintain the core infrastructure to allow machine learning engineers and data scientists to develop, train, evaluate, deploy, and operate Machine Learning models and pipelines, and power the GenAI products in Atlassian. You will use your software development expertise to solve difficult problems, tackling complex infrastructure and architecture challenges. You will have the opportunity to lead other engineers to drive involved projects from technical design to launch. You will also collaborate with other teams and internal customers to set expectations, gather input and communicate results. Compensation At Atlassian, we strive to design equitable, explainable, and competitive compensation programs. To support this goal, the baseline of our range is higher than that of the typical market range, but in turn we expect to hire most candidates near this baseline. Base pay within the range is ultimately determined by a candidate's skills, expertise, or experience. In the United States, we have three geographic pay zones. For this role, our current base pay ranges for new hires in each zone are: In The United States , we have three geographic pay zones. For this role, our current base pay ranges for new hires in each zone are: Zone A : $206,100 - $269,075 Zone B : $185,490 - $242,168 Zone C : $171,063 - $223,332 On your first day, we'll expect you to have 5+ years of experience as a software developer. Fluency in at least one modern object-oriented programming language (preferably Java/Kotlin and Python). Experience with Continuous Delivery and Continuous Integration. Experience building and operating large scale distributed systems using Amazon Web Services (S3, Kinesis, Cloud Formation, EKS, AWS Security and Networking). Experience with distributed large-scale data processing (preferably Apache Spark). Basic understanding of Machine Learning projects lifecycle. It would be great, but not required if you have Expert-level with search platform, deep learning training/inference platfrom. Experience with Databricks. Experience with scaling and deploying Machine Learning models.