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Atlassian provides tools to help every team unleash their full potential.
The Role
Lead the design and delivery of knowledge graph and machine learning systems that infer personal work context from connected tools. Build graph inference pipelines, schemas, permission-aware APIs, Rovo Chat integrations, and MCP-compatible CLI experiences. Establish evaluation frameworks, improve retrieval and grounding, provide technical direction across teams, mentor engineers, and ensure responsible, privacy-safe AI systems operate reliably at production scale.
Summary Generated by Built In
Engineering | Seattle, United States | Remote, Remote | Mountain View, United States or Remote | San Francisco, United States
Working at Atlassian
Atlassians can choose where they work - whether in an office, from home, or a combination of the two. That way, Atlassians have more control over supporting their family, personal goals, and other priorities. We can hire people in any country where we have a legal entity.
At Atlassian, we're on a mission to unleash the potential of every team. Central to that mission is the Teamwork Graph (TWG) - Atlassian's real-time, permissions-aware knowledge graph that unifies people, teams, projects, content, and activities across Atlassian and connected third-party tools. We believe the next frontier of AI-powered teamwork is personal working environment context: knowing who you collaborate with, what you're actively working on, and which documents matter right now - so that Rovo Chat, agents, and the TWG CLI can deliver answers that are precise, relevant, and actionable.
We're seeking a Principal Machine Learning Engineer (P60) to lead and design knowledge graph projects that build this personal working environment context layer and serve it at scale through Rovo Chat and the Teamwork Graph CLI.
What You'll Do
Build Personal Work Context Graphs
Improve Rovo Chat with Graph Context
Deliver Context Through Graph APIs & CLI
Lead Across Teams
At Atlassian, we strive to design equitable, explainable, and competitive compensation programs. We follow consistent hiring practices and account for each candidate's skills, knowledge, and experience when setting base pay within the range.
Please visit
Working at Atlassian
Atlassians can choose where they work - whether in an office, from home, or a combination of the two. That way, Atlassians have more control over supporting their family, personal goals, and other priorities. We can hire people in any country where we have a legal entity.
At Atlassian, we're on a mission to unleash the potential of every team. Central to that mission is the Teamwork Graph (TWG) - Atlassian's real-time, permissions-aware knowledge graph that unifies people, teams, projects, content, and activities across Atlassian and connected third-party tools. We believe the next frontier of AI-powered teamwork is personal working environment context: knowing who you collaborate with, what you're actively working on, and which documents matter right now - so that Rovo Chat, agents, and the TWG CLI can deliver answers that are precise, relevant, and actionable.
We're seeking a Principal Machine Learning Engineer (P60) to lead and design knowledge graph projects that build this personal working environment context layer and serve it at scale through Rovo Chat and the Teamwork Graph CLI.
What You'll Do
Build Personal Work Context Graphs
- 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.
Improve Rovo Chat with Graph Context
- Integrate personal context into Rovo Chat to improve relevance, groundedness, and efficiency.
- Build and measure context-selection strategies with the Rovo Chat team.
Deliver Context Through Graph APIs & CLI
- 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.
Lead Across Teams
- Provide technical leadership across Knowledge AI, Teamwork Graph, and product teams.
- Mentor engineers and champion responsible, privacy-safe AI and data quality.
At Atlassian, we strive to design equitable, explainable, and competitive compensation programs. We follow consistent hiring practices and account for each candidate's skills, knowledge, and experience when setting base pay within the range.
Please visit
Skills Required
- 8+ years of experience in ML/AI engineering
- Deep expertise in knowledge graphs, graph neural networks, or entity and relationship extraction
- Experience building and shipping ML-powered graph inference or knowledge representation systems at production scale
- Hands-on experience with graph databases such as Neo4j or Neptune, graph query languages such as Cypher or SPARQL, or large-scale graph processing frameworks such as GraphX, DGL, or PyG
- Experience shipping end-to-end ML features from data pipelines and model training through serving, monitoring, and iteration
- Strong understanding of LLM orchestration, retrieval-augmented generation, and context injection
- Experience designing inference pipelines using heterogeneous activity signals
- Proficiency in offline precision/recall benchmarks, online A/B testing, and human evaluation for ML systems
- Ability to set technical direction, drive architecture decisions, and communicate tradeoffs to engineering and product leadership
- Master's or PhD in Computer Science, Machine Learning, Information Retrieval, or a related field, or equivalent industry experience
- Experience with enterprise knowledge graphs, semantic embeddings, or ontology design at scale
- Familiarity with permission-aware data systems and privacy-by-design principles
- Background in collaboration analytics, social network analysis, or user activity modeling
What the Team is Saying
Tiffany
Engineering Senior Associate, United States
Gautam
Software Engineer, United States
Tygrr
Product Manager, United States
Ashleigh
Frontend Engineer, United States
Jensen
Senior Frontend Software Engineer, United States
Javier
Senior Solutions Engineer, United States
Atlassian Compensation & Benefits Highlights
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Parental & Family Support — Parental leave spans 26 weeks for birthing parents and 20 weeks for non-birthing parents, alongside inclusive family-formation benefits such as fertility treatment, adoption, and surrogacy. Additional supports include menopause resources and caregiving-related programs.
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Retirement Support — Retirement programs include a U.S. 401(k) company match with immediate vesting and multiple contribution options. These are complemented by other financial wellbeing tools like HSAs/FSAs.
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Healthcare Strength — Medical coverage is paired with mental-health resources, disability and life insurance, and options such as HSA/FSA. Descriptions also point to solid plan choices with access to counseling, coaching, and 24/7 support.
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The Company
What We Do
Atlassian creates teamwork solutions for high-performing teams. Our portfolio of collaboration and work management software products includes Jira, Confluence, Trello, Loom and Rovo. More than 300,000 businesses worldwide rely on Atlassian’s technology, including 80 percent of Fortune 500 companies. Our solutions support various business teams and they help organizations plan, track, and deliver their biggest ideas together.
Why Work With Us
At Atlassian, we believe we can accomplish so much more together than apart — which is why everything from our tooling — to our distributed workforce — to how our teams are structured is rooted in collaboration. Come join us and help unleash the potential of every team.
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