Senior ML Engineer - AI-Native Search & Evaluation

Atlassian; Seattle (WA); Hybrid; USD 206,000 - 269,000; Full time

Benefits offered by this job

Health and wellbeing resources

Paid volunteer days

Job summary

At Atlassian is seeking a Senior Machine Learning Engineer to join the Search & Intelligence organization. You will build AI-native experiences, agentic systems, models, evaluation frameworks, and data platforms powering Atlassian’s AI products.

You will own ML projects end-to-end—from problem definition and data development through experimentation, deployment, and continuous improvement—while focusing on evaluation, scalable architectures, and cross-functional collaboration.

Qualifications

• Bachelor's or Master's degree in CS/ML/DS or related field.
• At least 4 years of experience developing and deploying ML/AI systems in production.
• Strong Python skills; experience with Java, Kotlin, TypeScript.
• Experience with SQL and Spark for large-scale data processing.
• Experience building, evaluating, deploying, and scaling models with large datasets.
• Familiarity with cloud-based ML platforms such as AWS or Databricks.

Responsibilities

• Build and productionize machine learning models and AI systems for Search & Intelligence products.
• Own projects from concept through deployment, monitoring, evaluation, and iteration.
• Develop AI-native experiences across search, retrieval, ranking, recommendations, and agentic systems.
• Design evaluation-driven development processes with quality metrics, tests, benchmarks, and acceptance criteria.
• Mentor engineers and contribute to ML best practices.

Skills

Python

Java

Kotlin

TypeScript

SQL

Education

Bachelor's or Master's in CS/ML/DS or related field

Tools

Spark

Databricks

AWS

Job description

At Atlassian is seeking a Senior Machine Learning Engineer to join the Search & Intelligence organization. You will build AI-native experiences, agentic systems, models, evaluation frameworks, and data platforms powering Atlassian’s AI products.

You will own ML projects end-to-end—from problem definition and data development through experimentation, deployment, and continuous improvement—while focusing on evaluation, scalable architectures, and cross-functional collaboration.