Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

Leads Atlassian’s enterprise data governance operating model and trusted, AI-ready data strategy. Owns governance policies, domain ownership, decision rights, stewardship, data quality, catalog, metadata, lineage, and enterprise context architecture. Partners with Privacy, Legal, Security, and Compliance while implementing data contracts, automated classification, policy-as-code, and certification controls. Drives governance adoption across a federated organization and enables agents and applications to consume reliable, governed data.

Designs and maintains SaaS integrations, middleware, APIs, webhooks, automation scripts, and data pipelines. Optimizes enterprise software usage, supports AI integrations, troubleshoots complex technical issues, manages vendors and SLAs, tracks licensing and budgets, and translates business needs into technical requirements.

Lead product vision, strategy, and execution for Rovo Chat, Atlassian's AI-driven conversational interface. Prioritize adoption and quality, drive multi-turn conversation improvements, integrate knowledge across first- and third-party sources, partner cross-functionally to ship features, define success metrics, and champion responsible AI practices for safe, transparent conversational experiences.

Provides technical leadership on complex, high-impact engineering projects from design through launch. The role involves architecture, company-wide initiatives, engineering excellence, mentoring technical leaders, and establishing standards for scalable, reliable, secure, observable systems. The engineer will collaborate across teams, communicate complex technical topics, and apply customer-focused decision-making and A/B testing techniques.

Build and maintain scalable data pipelines, analytical data models, backend data systems, business rules, metrics, and data sources. Partner with teams across finance, growth, product, customer support, sales, and marketing to support analytical needs and improve data-driven decision-making. Own data engineering problems end to end and help advance organizational data practices.

Lead global mid-market sales performance and revenue growth for Atlassian’s Teamwork Collection. Develop operating plans, strategic sales plays, inspection rhythms, and scalable go-to-market execution. Partner across sales, SDR, channel, demand generation, product, and marketing teams to increase pipeline, accelerate deal velocity, and expand market presence. Enable account executives and sales managers through field readiness, onboarding, and clear positioning of collaboration and AI solutions.

Set technical vision and build AI foundations used across Atlassian: prototype and write code, build scalable AI infrastructure, measure AI effectiveness, provide context and cost-efficient compute, partner with product teams, and mentor senior engineers.

Build and improve backend services, REST APIs, monitoring, CI/CD pipelines, content-management integrations, and reliable customer-facing features. The role requires collaborative software development experience with prominent programming languages, REST integrations, Git, and server technologies such as Docker, NGINX, and Node.js. Experience with enterprise content management systems, AWS, Java, Freemarker, or Rust is advantageous.

Lead the design, development, deployment, and scaling of machine learning models and AI systems powering Atlassian products. Responsibilities include architecture design, experimentation, evaluation, collaboration with product and engineering teams, technical mentorship, and contributing to company-wide AI strategy. The role focuses on search, agentic AI, language models, and intelligent product capabilities.

Build and operate scalable machine learning systems for Atlassian’s Growth organization, including personalized recommendations, ranking, decisioning, experimentation, and customer journey optimization. Partner with product, engineering, data science, analytics, marketing, and sales teams to translate growth opportunities into measurable production capabilities. Develop datasets, features, models, evaluation frameworks, and monitoring systems while optimizing for customer value, fairness, quality, and long-term business outcomes.