> Markdown version of [/jobs/ext/4439585-principal-machine-learning-engineer](https://www.wearedevelopers.com/jobs/ext/4439585-principal-machine-learning-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal Machine Learning Engineer - **Company:** Atlassian - **Location:** Mountain View, CA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** A/B Testing, Application Programming Interfaces (APIs), Artificial Intelligence, Data Systems, Query Languages, Graph Database, Information Retrieval, Machine Learning, Neo4j, Real-Time Operating Systems, SPARQL, Graph Neural Networks, Ontology Design, Large Language Models, Social Network Analysis, Knowledge Representation, Information Technology, Atlassian Tools, Machine Learning Operations, Data Pipelines - **Published:** October 26, 2026 - **Apply:** https://www.careerbuilder.com/job-details/principal-machine-learning-engineer-mountain-view-ca--9d6594f7-d91c-4ef4-b399-c17b7ab14931 ## About the Role Machine Learning, Legal, Artificial Intelligence (AI), Team Player, Machine Learning, Application Programming Interface (API), MCP - Microsoft Certified Professional, Realtime Operating System, Team Lead/Manager, Technical Leadership, * 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 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.