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# Principal Machine Learning Engineer

- **Company:** Atlassian
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Salary:** $236,700 - $309,025
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Application Programming Interfaces (APIs), Artificial Intelligence, Data Systems, Query Languages, Graph Database, Information Retrieval, Machine Learning, Neo4j, SPARQL, Graph Neural Networks, Ontology Design, Large Language Models, Social Network Analysis, Knowledge Representation, Information Technology, Machine Learning Operations, Data Pipelines
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25304_3bca99c30f54d5a517753346ff806eab

## About the Role

* 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

 * Master's 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.

## Description

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.