As a Senior Machine Learning Engineer at Atlassian, you will design AI systems for Jira, solve complex ML challenges, collaborate with product teams, and ensure data-driven feature development.
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.
About the team
Jira is Atlassian's flagship work-tracking product and one of the most important systems of work in the world. Working on Jira Experience as an ML engineer means building the intelligence layer on top of that system of work. You get to design AI that orchestrates work between humans and machines: models that infer intent from messy, unstructured input; recommend the next best action across fragmented tools; and personalize workflows to the unique patterns of each team.
The problem space is both broad and deep - spanning large-scale retrieval and ranking, representation learning on the teamwork graph, multi-modal understanding across text and video, and human-in-the-loop systems embedded directly in product. It's a rare opportunity where solving hard ML problems doesn't just move an internal metric; it directly reshapes how the world's teams coordinate, collaborate, and ship work using Jira.
It would be great, but not required
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.
About the team
Jira is Atlassian's flagship work-tracking product and one of the most important systems of work in the world. Working on Jira Experience as an ML engineer means building the intelligence layer on top of that system of work. You get to design AI that orchestrates work between humans and machines: models that infer intent from messy, unstructured input; recommend the next best action across fragmented tools; and personalize workflows to the unique patterns of each team.
The problem space is both broad and deep - spanning large-scale retrieval and ranking, representation learning on the teamwork graph, multi-modal understanding across text and video, and human-in-the-loop systems embedded directly in product. It's a rare opportunity where solving hard ML problems doesn't just move an internal metric; it directly reshapes how the world's teams coordinate, collaborate, and ship work using Jira.
- 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
- 8+ years of overall experience and 4+ years working in Artificial Intelligence and Machine Learning
- 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, 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
Top Skills
Java,Kotlin,Python,Aws,Databricks,Apache Spark
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