24 hours ago
Remote, AustraliaSenior
Responsibilities
- Design and evaluate ML approaches for retrieving relevant customer feedback evidence.
- Develop retrieval, clustering, and language-model pipelines for discovering themes across feedback.
- Improve AI-generated tags, field suggestions, and connections using customer acceptance, rejection, and edit signals.
- Turn product problems into testable hypotheses, experiments, baselines, and representative evaluation datasets.
- Assess quality across customer segments and dataset sizes, including sparse and imbalanced data.
- Balance model quality, latency, inference cost, and operational complexity, including evaluating fine-tuning where beneficial.
- Take ML capabilities from experimentation into production with regression checks, monitoring, controlled rollouts, and failure diagnosis.
- Collaborate with product, design, engineering, and platform teams to interpret customer feedback and prioritize improvements.
- Develop reusable evaluation methods and tooling for other Product Collection use cases.
Requirements
- Experience delivering applied ML systems beyond prototypes, including product integration, evaluation, monitoring, or post-release improvement.
- Expertise in Python and the ability to write performant, production-quality code.
- Familiarity with SQL, Spark, and cloud data environments such as AWS or Databricks.
- Practical experience in one or more of information retrieval, semantic search, natural language processing, clustering, or LLM-based applications.
- Ability to construct evaluation datasets, select metrics, compare baselines, and investigate differences between offline and customer outcomes.
- Understanding of data leakage, selection bias, imbalanced data, sparse labels, and overfitting.
- Awareness of latency, inference cost, observability, and changing data in production ML systems.
- Clear communication and collaboration skills across product, design, and engineering disciplines.
- Useful additional experience includes retrieval-augmented generation, chunking, embeddings, hybrid search, reranking, theme discovery, human evaluation, user-feedback learning, cold-start scenarios, online experiments, fine-tuning, inference optimization, and reusable ML evaluation tooling.
Benefits
- Flexible work arrangement allowing employees to work from an office, from home, or a combination of both.
- Atlassian can hire people in any country where it has a legal entity.
Tech Stack
Categories
About Atlassian
Atlassian powers the collaboration that helps teams accomplish what would otherwise be impossible alone. From space missions and motor racing to bugs in code and IT requests, no task is too large or too small with the right team, the right tools, and the right practices. Over 300,000 global companies and 80% of the Fortune 500 rely on Atlassian’s software, like Jira, Confluence, Loom, and Trello, to help their teams work better together and deliver quality results on time. With our 300,000+ customers and team of 10,000+ Atlassians, we are building the next generation of team collaboration and productivity software. We believe the power of teams has the potential to change the world — one that is more open, authentic, and inclusive.