Working at Atlassian
Atlassians have flexibility in 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.
Atlassian is seeking a Senior Machine Learning Scientist to join our Central AI team located in Bellevue WA. The Central AI organization constructs the fundamental infrastructure, data pipeline, frameworks, models, and other capabilities to expedite AI feature development throughout the entire company.
Your future team
The Central AI org is part of the larger Atlassian Intelligence program. Its purpose is to accelerate AI innovation across all our products and platform, provide cohesive AI experiences and setup up an Atlassian AI infrastructure for the future.
While it's anticipated that the majority of AI-driven initiatives will be developed and executed by federated product teams within Atlassian, the role of Central AI is pivotal to this process. Central AI is tasked with constructing the underlying infrastructure and capacities, which are crucial for the seamless integration and optimal functionality of AI across various departments. By doing so, Central AI ensures that different teams within the organization do not work in silos but have access to a unified foundation that promotes efficiency and collaboration. Additionally, Central AI is responsible for developing some of the core shared experiences typical in the AI domain, such as search, knowledge discovery and conversation.
What you'll do
As a Senior Machine Learning engineer, you will work on the development and implementation of the cutting edge machine learning algorithms, training sophisticated models, collaborating with product, engineering, and analytics teams, to build the AI functionalities into each Atlassian products and services. Your daily responsibilities will encompass a broad spectrum of tasks such as designing system and model architectures, conducting rigorous experimentation and model evaluations, and providing guidance to junior ML engineers. Your role is pivotal, stretching beyond these tasks, ensuring AI's transformative potential is realized across our offerings.
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.
Your background
On the first day, we'll expect you to have

• Bachelor's or Master's degree (preferably a Computer Science degree or equivalent experience)
• 3+ years of related industry experience in the data science domain
• Expertise in Python or Java with and the ability to write performant production-quality code, familiarity with SQL, knowledge of Spark and cloud data environments (e.g. AWS, Databricks)
• Experience building and scaling machine learning models in business applications using large amounts of data
• Ability to communicate and explain data science concepts to diverse audiences, craft a compelling story
• Focus on business practicality and the 80/20 rule; very high bar for output quality, but recognize the business benefit of "having something now" vs "perfection sometime in the future"
• Agile development mindset, appreciating the benefit of constant iteration and improvement

It's great, but not required, if you have

• Experience working in a consumer or B2C space for a SaaS product provider, or the enterprise/B2B space
• Experience in developing deep learning-based models and working on LLM-related applications
• Excelling in solving ambiguous and complex problems, being able to navigate through uncertain situations, breaking down complex challenges into manageable components and developing innovative solutions