Note: The job is a remote job and is open to candidates in USA. Atlassian develops collaboration software designed to help teams work together and achieve their potential. The Senior Machine Learning Engineer will design, build, and operate scalable ML systems for personalized recommendations, engagement and activation journeys, cross-product expansion, and sales experiences. The role also involves defining ML strategy, partnering with cross-functional teams, conducting experiments, and optimizing customer and business outcomes across the growth funnel.
Responsibilities
- 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
Skills
- 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
- 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
- Eligibility for benefits, bonuses, commissions, and equity
- Choice of working in an office, from home, or a combination of the two
- Health and wellbeing resources
- Paid volunteer days
Company Overview
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