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The Enterprise Security Engineer is a hands-on role that implements, operates, and improves security controls across identity, endpoints, cloud, data, and workforce technologies, turning enterprise strategy into practical, secure-by-default solutions. They partner with Enterprise IT, Corporate Engineering, SRE and Platform teams, Business Systems, Product Security, Legal, Privacy, and other stakeholders, and participate in Atlassian’s Customer Zero initiative to validate capabilities internally before broader adoption; the role is based in Australia/New Zealand. Core responsibilities include designing, implementing, operating, and maintaining controls across identity, access, cloud (AWS/GCP), SaaS, and data-protection domains, remediating gaps in access controls, device posture, SaaS configurations, and sensitive data protections, and assisting with secure baseline configurations and reviews for new tools and workflows. The role also supports cyber-physical security operations, including integrations between physical access-control systems and identity platforms, monitoring access lifecycles, and addressing facility-related cyber risks, while strengthening identity lifecycle management, privileged access, authentication, authorization, and access reviews. Success is measured by timely execution of technical tasks and deployments, reduced security backlog and toil, effective automation and tooling, closed posture gaps, and strong cross-functional collaboration and learning.

The Enterprise Security Engineer is a hands-on role that implements, operates, and improves security controls across corporate architecture, SaaS, cloud, endpoints, identity, data, and workforce technologies, translating strategy into practical, secure-by-default controls that reduce risk without slowing the business. They partner with Enterprise IT, Corporate Engineering, SRE, Platform teams, Business Systems, Product Security, Legal, Privacy, and other stakeholders to deliver scalable security improvements across Atlassian’s corporate environment, and as part of Atlassian’s Customer Zero initiative validate and refine capabilities internally before broader adoption, based in Australia/New Zealand. The role covers designing, implementing, operating, and maintaining security controls across identity, access, cloud (AWS/GCP), SaaS, endpoints, and data-protection domains, remediating gaps in access controls, device posture, SaaS configurations, and SaaS supply chain/third-party risk evaluations, and assisting with secure baselines and paved-path implementations. It includes security reviews for new tools, internal workflows, and SaaS integrations; monitoring insider risk signals and anomalous access patterns; enforcing service accounts and automated bot credentials; implementing guardrails, DLP, and data-classification controls; maintaining physical security integrations with identity platforms and monitoring facility-related cyber risks; and participating in on-call rotations and post-incident reviews. Success is measured by execution and delivery of scoped tasks and automation milestones on time; triage of security escalations and backlog reduction; development of reliable scripts and tooling; remediation of posture gaps; and collaboration to expand technical depth across enterprise security domains.

Atlassian’s mission is to unleash the potential of every team, with well-known products like Jira, Confluence, and Bitbucket that power collaboration across diverse teams and industries, as illustrated by examples like NASA’s Mars Rover and Cochlear. The role sits in the Central AI Org, specifically the AI & ML Platform team, which is dedicated to driving AI innovation across Atlassian products and building a robust AI infrastructure for the future. The Central AI Org aims to develop horizontal AI capabilities, establish a centralized Search, Q&A, and Conversational AI system, and explore integrating Atlassian products with AI solutions beyond the Atlassian ecosystem. The AI & ML Platform Team’s goal is to democratize AI and ML for Atlassian’s teams, customers, and ecosystem by delivering productive, reliable tools that integrate with the Atlassian Data Platform and adhere to security and data usage policies. As a Senior Machine Learning System Engineer, you will develop and refine core infrastructure to enable engineers and data scientists to create, train, evaluate, deploy, and manage ML models, collaborate with Jira and Confluence teams, curate high-quality datasets, fine-tune LLMs (open-source or proprietary), lead projects from design to launch, deliver scalable AI features, perform code reviews and documentation, and mentor junior team members.

1) Atlassian offers flexible work options (office, remote, or hybrid) and hires in any country where it has a legal entity. 2) Atlassian is seeking a Senior Machine Learning Engineer to join the AI Context team within the broader Rovo + AI organization to build agentic LLM harnesses powering high-visibility AI features and contextually aware experiences for users and agents. 3) The AI Context team spans Sydney, Melbourne, Brisbane, and the US West Coast, consisting of software engineers and MLEs who partner across Rovo + AI and product teams to deliver insights, modeling, and personalization that provide relevant context. 4) Responsibilities include designing, building, shipping, and operating agentic LLM harness solutions; improving quality and reducing cost of production LLM systems; building evaluation capabilities; contributing to fine-tuning and model serving; setting long-term technical direction; navigating complex systems; leading small virtual squads; and coaching junior engineers. 5) The role emphasizes ownership, autonomy, asynchronous collaboration, and the opportunity to influence technical direction across stakeholder teams.