Atlassian
Senior Machine Learning Engineer - Growth
$171K–$269K
Seattle, United States +2 · Senior · Full-time · Posted Oct 28, 2026
- Location
- Seattle, United States
Remote, Remote
San Francisco, United States - Experience
- 5+ years of related industry experience in the machine learning domain.
- Education
- Bachelor's or Master's degree (preferably a Computer Science degree or equivalent experience).
About the job
Designs, builds, and operates machine learning systems that personalize recommendations and customer and sales journeys across Atlassian’s growth funnel.
What you'll do
- Build models, data foundations, evaluation frameworks, and decisioning services for recommendations, engagement, activation, expansion, and sales experiences.
- Run offline evaluations and online experiments; monitor attribution, latency, quality, and fairness, and iterate based on outcomes.
- Partner with product, engineering, data science, analytics, marketing, and sales teams to turn growth opportunities into measurable production capabilities.
What they ask for
- Bachelor's or Master's degree, preferably in Computer Science, or equivalent experience.
- 5+ years of related industry experience in the machine learning domain.
- Expertise in Python; familiarity with SQL, Spark, cloud data environments, and production-quality code.
- Experience scaling ML models for business applications with large datasets, building large-scale datasets and evaluations, and partnering with teams on personalized journeys or sales-assist experiences.
Nice to have
- Experience in consumer/B2C SaaS or enterprise/B2B.
- Experience applying ML to growth, personalization, recommendations, ranking, experimentation, or decisioning across a customer funnel.
Pay
$206,100 - $269,075 USD base salary (Zone A); $185,490 - $242,168 USD base salary (Zone B); $171,063 - $223,332 USD base salary (Zone C)
Tools
Python, Java, Typescript, SQL, Spark, AWS, Databricks
AI focus
Machine learning for personalized recommendations and journeys, growth-funnel decisioning, ranking, and sales experiences; includes contextual bandits, uplift modeling, recommender systems, policy evaluation, and causal inference.
Model-written summary · the original posting is authoritative