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

Lead development of Atlassian’s Digital Success OS by defining data foundations, telemetry pipelines, AI-agent signals, journey orchestration, and digital-to-human handoffs. Partner with data engineering, analytics, data science, AI, RevOps, and customer success teams to operationalize customer plays, predictive models, experimentation, measurement, and feedback loops. Own cross-functional decisions, architecture principles, dependencies, and change control while improving adoption, engagement, churn-risk detection, agent effectiveness, and customer outcomes at scale.

Build and maintain core infrastructure for developing, training, evaluating, deploying, and operating machine learning models and pipelines for Atlassian’s GenAI products. Solve complex software infrastructure and architecture challenges, lead engineers through technical projects from design to launch, and collaborate with internal teams and customers. The role involves large-scale distributed systems, data processing, cloud platforms, CI/CD, and machine learning lifecycle support.

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.

Leads Atlassian’s investor relations strategy, including corporate narrative, quarterly earnings, financial disclosures, executive briefings, investor and analyst engagement, Investor Day, market sentiment analysis, and cross-functional messaging. Partners with Finance, Legal, Communications, Product, Strategy, Corporate Development, and Data Science while representing the company with institutional investors and Wall Street analysts.

Develop and productionize machine learning systems for search, retrieval, ranking, recommendations, conversational experiences, and AI agents. Own projects across the ML lifecycle, build evaluation datasets and benchmarks, conduct offline and online evaluations, analyze model quality, and improve system performance. Design scalable architectures addressing reliability, latency, privacy, and cost. Collaborate across product and engineering teams, communicate technical decisions, mentor engineers, and contribute to ML best practices.

Leads Atlassian’s global detection and response organization, overseeing security strategy, detection engineering, 24x7 incident response, threat intelligence, hunting, security operations, and automation. Builds cloud-native detection capabilities, improves telemetry and security data platforms, responsibly applies ML and LLMs, and establishes governance, metrics, compliance readiness, executive communications, and customer trust across global teams.

The job description does not include specific responsibilities or qualifications. The role is identified as Principal Data Scientist and may be performed remotely, in an office, or in a hybrid arrangement, depending on the employee’s location and Atlassian’s legal entity availability.

Conduct workplace investigations, advise leaders on performance and employee relations matters, resolve conflicts, interpret employment policies and laws, analyze ER trends, recommend systemic improvements, and deliver training on performance management, difficult conversations, and compliance.

Lead a distributed engineering organization building developer infrastructure, AI measurement and evaluation frameworks, context retrieval pipelines, agentic workflow infrastructure, and developer tooling. Manage engineering managers and senior engineers, shape technical roadmaps, optimize reliability and costs, partner with product and executive leadership, and foster a high-trust engineering culture.

Own the strategy, roadmap, quality, and launch of Confluence’s connected-content aggregation and AI-powered knowledge experiences. Define content organization, freshness, permissions, retrieval quality, and model evaluations. Lead cross-functional execution across engineering, design, data science, research, marketing, partnerships, and security. Drive product decisions for undefined problems, mentor PMs, and uphold quality standards for trustworthy enterprise content and AI answers.