Principal Machine Learning Engineer (Knowledge Graph)

About the role

Atlassian is looking for a Principal Machine Learning Engineer (P60) to lead and design knowledge graph projects that build a personal working environment context layer, serving it at scale through Rovo Chat and the Teamwork Graph CLI. The role is open to Seattle, Mountain View or San Francisco, with remote options available.

Key responsibilities

• Build personal work context graphs by designing graph inference pipelines that surface collaborators, active work, documents and projects from connected tools, and define schemas, permissions and evaluation frameworks for reliable inferred context.
• Integrate personal context into Rovo Chat to improve relevance, groundedness and efficiency, and build and measure context-selection strategies with the Rovo Chat team.
• Build low-latency, permission-safe APIs and CLI experiences for personal work context, enabling 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.

Required profile

• 8+ years in ML/AI engineering, with deep expertise in knowledge graphs, graph neural networks or entity/relationship extraction.
• Proven track record of shipping ML-powered graph inference or knowledge representation systems at production scale.
• Demonstrated ability to ship end-to-end ML features, from data pipeline and model training through serving, monitoring and iteration.
• Master's or PhD in Computer Science, Machine Learning, Information Retrieval or a related field preferred, or equivalent industry experience.

Required skills

• Graph databases (Neo4j, Neptune or equivalent), graph query languages (Cypher, SPARQL) or large-scale graph processing frameworks (GraphX, DGL, PyG).
• LLM orchestration, retrieval-augmented generation (RAG) and context injection, including how graph-derived context improves LLM grounding.
• Offline precision/recall benchmarks, online A/B testing and human evaluation for ML systems.
• Nice to have: enterprise knowledge graphs, semantic embeddings, ontology design, permission-aware data systems and background in collaboration analytics or social network analysis.

What we offer

• Base pay for new hires in the US: Zone A $236,700-$309,025, Zone B $213,030-$278,123, Zone C $196,461-$256,491.
• Possible eligibility for benefits, bonuses, commissions and equity.
• Health and wellbeing resources, paid volunteer days and other perks.