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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:
Zone A: $199,400 - $265,800
Zone B: $179,400 - $239,200
Zone C: $165,500 - $220,600
This role may also be eligible for benefits, bonuses, commissions, and equity.
Please visit go.atlassian.com/payzones for more information on which locations are included in each of our geographic pay zones. However, please confirm the zone for your specific location with your recruiter.
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.
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.
Expert-level with search platform, deep learning training/inference platfrom.
Experience with Databricks.
Experience with scaling and deploying Machine Learning models.
