Senior Machine Learning Engineer, Growth

Your future team

The Growth organization brings together product, engineering, data science, analytics, and product operations to improve how customers discover, adopt, and expand their use of Atlassian. We work across the full funnel, from acquisition and onboarding through activation, conversion, expansion, and retention. Our teams use experimentation, personalization, and intelligent orchestration to personalize and help customers see the right product, feature, or next step at the right time, all while creating durable business value.

As part of Growth, you will build ML capabilities that enable teams to deliver personalized and consistent, measurable experiences across Atlassian’s portfolio. This includes building reliable data foundations, features, models, evaluation frameworks, and decisioning systems that support cross-product recommendations and personalized journeys; for example, moving cross-flow ranking from fragmented, fixed heuristics toward a unified, value-aware orchestration system that ranks recommendations using user context, probability of conversion, and expected lifetime value, going from generic, noisy messaging to actionable and personalized recommendations that improve customer outcomes and business impact across surfaces such as navigation, screen-space flags, and app-switcher experiences.

What you’ll do

As a Machine Learning Engineer, you will design, build, and operate ML systems that help Growth make better decisions across the customer and sales funnel. You will develop models and decisioning services for personalized recommendations, engagement and activation journeys, cross-product expansion, and sales experiences, providing contextual, proactive support throughout the funnel. You will define the strategy on how our ML capabilities should evolve to support Atlassian short, mid, and long-term goals and drive their implementation.

You will partner closely with product, engineering, data science, analytics, marketing, and sales teams to translate ambiguous growth opportunities into measurable experiments and production capabilities. Your work may include designing models and system architectures; building datasets, features, and evaluation frameworks; running offline policy evaluation and online experiments; monitoring attribution, latency, quality, and fairness; and iterating based on customer and outcomes. You will help ensure that we optimize for durable customer and portfolio value, not only short-term clicks or conversions.

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)

• 5+ years of related industry experience in the machine learning domain

• Expertise in Python, and knowledge about other languages such as Java and Typescript, with 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

• Experience building datasets and evals to benchmark systems operating at a large scale

• Ability to communicate and explain ML 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 in solving ambiguous and complex problems, being able to navigate through uncertain situations, breaking down complex challenges into manageable components, and developing innovative solutions

• Experience partnering with product, analytics, marketing, or sales teams to build personalized customer journeys or sales-assist experiences

• Familiarity with contextual bandits, uplift modeling, recommender systems, policy evaluation, causal inference, or other approaches for optimizing decisions under uncertainty

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 applying machine learning to growth, personalization, recommendations, ranking, experimentation, or decisioning problems across a customer funnel

• Understanding of engagement, activation, conversion, expansion, and retention metrics, and how to balance short-term signals with downstream outcomes

Benefits & Perks

Atlassian offers a wide range of perks and benefits designed to support you, your family and to help you engage with your local community. Our offerings include health and wellbeing resources, paid volunteer days, and so much more. To learn more, visit go.atlassian.com/perksandbenefits.

About Atlassian

At Atlassian, we're motivated by a common goal: to unleash the potential of every team. Our software products help teams all over the planet and our solutions are designed for all types of work. Team collaboration through our tools makes what may be impossible alone, possible together.

We believe that the unique contributions of all Atlassians create our success. To ensure that our products and culture continue to incorporate everyone's perspectives and experience, we never discriminate based on race, religion, national origin, gender identity or expression, sexual orientation, age, or marital, veteran, or disability status. All your information will be kept confidential according to EEO guidelines.

To provide you the best experience, we can support with accommodations or adjustments at any stage of the recruitment process. Simply inform our Recruitment team during your conversation with them.

To learn more about our culture and hiring process, visit go.atlassian.com/crh.

In line with local law, identity verification (which may include use of biometric data) is a condition of employment with Atlassian for employment fraud purposes.