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About the Role
We're seeking a Principal Machine Learning Engineer (P60) to lead and design knowledge graph projects that build a personal working environment context layer and serve it at scale through Rovo Chat and the Teamwork Graph CLI.
Responsibilities
- Design graph inference pipelines that surface collaborators, active work, documents, and projects from connected tools.
- Define schemas, permissions, and evaluation frameworks for reliable inferred context.
- Integrate personal context into Rovo Chat to improve relevance, groundedness, and efficiency.
- Build and measure context-selection strategies with the Rovo Chat team.
- Build low-latency, permission-safe APIs and CLI experiences for personal work context.
- Enable MCP-compatible agents to query a user’s work environment in real time.
- Provide technical leadership across Knowledge AI, Teamwork Graph, and product teams.
- Mentor engineers and champion responsible, privacy-safe AI and data quality.
Requirements
- 8+ years in ML/AI engineering, with deep expertise in knowledge graphs, graph neural networks, or entity/relationship extraction.
- Proven track record of building and shipping ML-powered graph inference or knowledge representation systems at production scale.
- Hands-on experience with one or more of: graph databases (Neo4j, Neptune, or equivalent), graph query languages (Cypher, SPARQL), or large-scale graph processing frameworks (GraphX, DGL, PyG).
- Demonstrated ability to ship end-to-end ML features — from data pipeline and model training through serving, monitoring, and iteration.
- Strong understanding of LLM orchestration, retrieval-augmented generation (RAG), and context injection — specifically how graph-derived context improves LLM grounding and relevance.
- Experience designing inference pipelines that derive implicit entities and relationships from heterogeneous activity signals (work items, documents, projects, code changes).
- Proficiency in evaluation methodology: offline precision/recall benchmarks, online A/B testing, and human evaluation for ML systems.
- Ability to set technical direction across teams, drive architecture decisions, and communicate tradeoffs clearly to engineering and product leadership.
Skills
- Knowledge Graphs
- Graph Neural Networks
- Entity/Relationship Extraction
- Graph Databases (Neo4j, Neptune, or equivalent)
- Graph Query Languages (Cypher, SPARQL)
- Large-scale Graph Processing Frameworks (GraphX, DGL, PyG)
- LLM Orchestration
- Retrieval-Augmented Generation (RAG)
- Context Injection
- Evaluation Methodology
- Offline Precision/Recall Benchmarks
- Online A/B Testing
- Human Evaluation for ML Systems
Location
- Any country where Atlassian has a legal entity
Work Type
- Office
- Home
- Combination of office and home
Experience Level
- Principal
Education Level
- Master's or PhD in Computer Science, Machine Learning, Information Retrieval, or related field preferred — or equivalent industry experience.
Salary/Compensations
- Zone A: $236,700 - $309,025
- Zone B: $213,030 - $278,123
- Zone C: $196,461 - $256,491
Benefits
- Health and wellbeing resources
- Paid volunteer days
About the Company
- Atlassian, we're on a mission 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.
Equal Opportunity
- 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.