Overview

We9re 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.

Responsibilities

What You9ll 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 user9s 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.

Compensation

At Atlassian, we strive to design equitable, explainable, and competitive compensation programs. We follow consistent hiring practices and account for each candidate9s skills, knowledge, and experience when setting base pay within the range.

Please visit go.atlassian.com/payzones for more information on which locations are included in each of our geographic pay zones. However, please confirm the zone for your specific location with your recruiter.

This role may also be eligible for benefits, bonuses, commissions, and equity.

Pay Ranges

In The United States, we have three geographic pay zones. For this role, our current base pay ranges for new hires in each zone are:

Zone A: $236,700 - $309,025

Zone B: $213,030 - $278,123

Zone C: $196,461 - $256,491

Qualifications

What We9re Looking For

Experience

• 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.

Skills

• 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.

Education

• Master9s or PhD in Computer Science, Machine Learning, Information Retrieval, or related field preferred - or equivalent industry experience.

Nice to Have

• Experience with enterprise knowledge graphs, semantic embeddings, or ontology design at scale.
• Familiarity with permission-aware data systems and privacy-by-design principles for user-centric inference.
• Background in collaboration analytics, social network analysis, or user activity modeling.

Benefits & Perks

Atlassian offers a wide range of perks and benefits designed to support you, your family and to help you engage with your local community. Our offerings include health and wellbeing resources, paid volunteer days, and so much more. To learn more, visit go.atlassian.com/perksandbenefits