Jobs at Atlassian

Leads Atlassian’s AMER Credit and Collections function, managing teams and overseeing credit assessment, collections, disputes, AR accounting, reconciliations, controls, policies, audits, and process improvements. Partners with Sales, Accounting, Revenue, IT, and Customer Support to manage risk, improve cash performance, and support quote-to-cash operations. Uses portfolio insights and metrics such as DSO, aging, delinquency, and write-offs to drive decisions and operational improvements.

Leads a multi-year trust portfolio spanning security, compliance, GRC, privacy, and regulated-market readiness. Coordinates engineering, product, legal, IT, and governance teams; drives secure-by-default development, software supply chain security, automated controls, vulnerability monitoring, and regulatory authorizations. Advises executives on technical risk and trade-offs while establishing governance frameworks, metrics, evidence pipelines, and dependency-management mechanisms to demonstrate measurable risk reduction.

Lead the 0-to-1 design vision for Atlassian’s AI-native Strategy Collection. Manage and scale a team of 5–10 designers, establish high standards for product craft, partner with Product and Engineering leadership, champion AI-enabled design workflows, and create intuitive experiences for executive and portfolio-management users.

Own the architecture and technical direction for Atlassian’s enterprise agentic search platform. Design retrieval, agent orchestration, context construction, evaluation, LLM training, serving, and continuous improvement systems. Address enterprise permissions, tenant isolation, freshness, scalability, latency, cost, privacy, and reliability. Validate architecture through prototypes and production implementations, establish benchmarks and feedback loops, guide cross-team roadmaps, influence technical adoption, and mentor senior engineers and scientists.

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.

Lead complex technical programs spanning platform and product components. Define long-term strategies, roadmaps, technical objectives, and action plans; translate business needs into prioritized systems problems; influence engineers and architects; manage dependencies, budgets, schedules, forecasting, and end-to-end program lifecycles. Programs may involve agentic platforms, machine learning, enterprise search, conversational AI, reliability, performance, and enterprise readiness.

Design and optimize large-scale machine learning model-serving systems, including distributed infrastructure, load balancing, auto-scaling, batching, caching, and GPU inference optimization. Build reliable, high-concurrency services handling billions of requests, benchmark and accelerate inference engines, develop CI/CD for model deployment, and partner with ML engineers to fine-tune and deploy open-source large language models.

Own the roadmap and delivery of Atlassian Government Cloud, balancing customer value, compliance, engineering investment, and market impact. Translate FedRAMP and NIST controls into product requirements, lead cross-functional execution, engage public sector customers, support migrations, and guide pricing, packaging, commercialization, Marketplace readiness, and launch activities. Serve as a subject matter expert on government cloud needs and compliance requirements.

Leads application and software engineering teams supporting Oracle Fusion Cloud and core Finance SaaS applications. Oversees finance transformations, integrations, automation, architecture, reliability, quarterly Fusion upgrades, and company-wide initiatives. Partners with Finance and Engineering stakeholders, drives metrics and OKRs, maintains SOX compliance, leads code reviews and complex problem resolution, and advances AI/ML and continuous improvement across critical financial systems.

Leads security incident response across corporate environments and cloud services, investigating threats, analyzing logs, conducting threat hunts and forensic analysis, and coordinating incident resolution. Develops response tools, automation, playbooks, and standard operating procedures; performs post-incident reviews and tabletop exercises; researches threat actors and improves organizational security posture through cross-functional collaboration.

Manages accounts receivable collections, delinquency resolution, billing disputes, payment plans, cash application, refunds, reconciliations, aging analysis, bad-debt provisioning, reporting, audit support, and process improvements. Collaborates with global customers, partners, accounting teams, sales operations, and management while maintaining collection KPIs and supporting team development.

Own financial planning, forecasting, budgeting, and modeling for Atlassian’s AI infrastructure and token spend. Partner with Engineering, Product, Business Operations, and Finance leadership to evaluate AI economics, usage, vendor pricing, infrastructure requirements, COGS, unit economics, and gross margins. Drive scenario analysis, long-range planning, process improvements, and scalable financial models while translating complex technical and financial data into actionable executive insights.

Conduct an AI-focused research project and contribute tools, pipelines, and systems for Atlassian’s Research and Insights organization. The intern will help develop AI-enabled research workflows, collaborate with a research manager, and support faster insight generation for cross-functional teams. The 12-week, full-time internship is based at Atlassian’s Seattle hub and includes mentorship, technical training, and professional development.

Security Engineering Intern responsible for completing a scoped security project, developing automation and investigation tools, researching vulnerabilities, analyzing findings, validating AI-assisted outputs, collaborating with engineers, and documenting and demonstrating solutions. The role requires programming, web and systems knowledge, foundational security understanding, AI familiarity, and current enrollment in a bachelor’s or master’s program.

Own customer-facing products end to end across the full stack, from React and TypeScript interfaces to backend services and APIs. Shape product and architecture decisions, use telemetry to measure outcomes, and build AI-enabled and agentic experiences with LLMs. Set technical direction, collaborate with product and design teams, mentor engineers, and drive ambiguous prototypes into reliable, scalable production systems.

Conduct statistical analysis, evaluate product experiments, analyze user interaction data, identify growth opportunities, and build dashboards, notebooks, or AI agents. Partner with data science, engineering, and business teams to communicate actionable insights. The internship requires full-time participation for 12 weeks in the Seattle hub and enrollment in a master's program with graduation by August 2028.

Own customer-facing features end to end across React/TypeScript front ends, backend services, and APIs. Make product-driven architecture decisions, use telemetry to improve outcomes, and build AI-enabled and agentic experiences with LLMs. Set technical direction, influence product strategy, mentor engineers, and drive ambiguous prototypes into reliable, scalable production systems.

Own customer-facing product features across the full stack, from ambiguous problems through measurable outcomes. Make architecture and product decisions, use telemetry to guide design, and build AI-enabled and agentic experiences integrating LLMs. Set technical direction, influence product strategy, mentor engineers, and drive prototypes into reliable, performant production systems at scale.

Lead architecture and delivery of a secure, low-latency Kubernetes-based compute platform for AI agents and untrusted workloads. Own isolation, scheduling, lifecycle, networking, policy, observability, and control-plane capabilities across GCP and AWS. Drive cross-team outcomes from design to adoption, improve developer experience via APIs and automation, and champion reliability, security, performance, and cost efficiency while mentoring engineers and setting technical direction.

Lead machine learning architecture and technical direction across multiple quarters while remaining involved in experimentation, quality, and production systems. Work across model post-training, personalization and recommendation, and agentic experiences. Define solutions for ambiguous problems, align cross-functional partners, drive execution, and safely bring ML systems into production.