Senior Machine Learning 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.

Core Responsibilities
As a Senior Machine Learning Engineer, you will design, build, and deploy scalable ML solutions that address core business and product challenges across multiple domains. You'll own projects end-to-end, from data exploration and feature engineering through training, evaluation, deployment, and monitoring in production. A key part of the role is ensuring deployed models perform reliably, with continuous monitoring, troubleshooting, and improvements.

Collaboration and Leadership
You'll collaborate closely with product managers, data scientists, engineers, and stakeholders to identify opportunities for AI/ML impact and deliver high-quality solutions. Beyond individual contributions, you'll provide technical leadership by mentoring junior engineers, reviewing code, and driving best practices in reproducibility, testing, and documentation.

Innovation and Enablement
The role also emphasizes innovation and enablement. You'll stay current with research and advancements in AI/ML, introducing new techniques and tools where valuable. You'll also develop reusable frameworks, components, and guidelines to help teams across the organization adopt ML effectively. Strong communication is essentialboth in documenting methodologies and results and in explaining technical concepts to diverse audiences.

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

• 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