Senior ML Systems Engineer, GenAI Platform (Remote/Hybrid)

Atlassian; United States; Hybrid; USD 206,000 - 269,000; Full time

Job summary

Atlassian is seeking a Senior ML System Engineer for the Rovo GenAI Platform to build and maintain the core infrastructure that lets ML engineers and data scientists develop, train, evaluate, deploy, and operate models and pipelines powering GenAI products.

You will lead other engineers through design to launch, tackle tough infra and architecture challenges, and collaborate across teams and with internal customers to set expectations and share results.

Qualifications

• 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).

Responsibilities

• 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.
• Lead other engineers to drive involved projects from technical design to launch.
• Collaborate with other teams and internal customers to set expectations, gather input and communicate results.

Skills

Software development

Java/Kotlin/Python

CI/CD

Distributed systems

Apache Spark

Tools

AWS

S3

Kinesis

CloudFormation

EKS

Databricks

Job description

Atlassian is seeking a Senior ML System Engineer for the Rovo GenAI Platform to build and maintain the core infrastructure that lets ML engineers and data scientists develop, train, evaluate, deploy, and operate models and pipelines powering GenAI products.

You will lead other engineers through design to launch, tackle tough infra and architecture challenges, and collaborate across teams and with internal customers to set expectations and share results.