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

Principal Machine Learning Engineer responsible for setting technical direction and building production-grade AI systems for Confluence. The role spans model evaluation, retrieval, ranking, prompt and workflow design, experimentation, reliability, and customer-facing AI product development. Responsibilities include making architectural decisions, partnering across engineering and product teams, mentoring engineers, identifying model and product failure modes, and delivering scalable AI experiences across content creation, editing, discovery, recommendations, and multimodal interaction.

Develop and scale digital and agentic customer success programs that improve product adoption and retention. Coordinate messaging across email, in-app, community, learning, AI agents, and human channels. Partner with Product, Data Science, Marketing, and Customer Success to use product signals, optimize content distribution, enable expansion, and connect digital experiences with frontline teams. Lead experimentation and cross-functional change through data-backed hypotheses and continuous improvement.

Lead Advisory Services engagements for strategic and enterprise clients using Atlassian solutions. Serve as the primary client contact, manage scope, resources, projects, and programs, accelerate time to value, monitor outcomes, and identify expansion opportunities. Build long-term client relationships, communicate value, and collaborate with internal Atlassian teams to deliver solutions. Travel domestically and internationally up to 30% of the time.

Lead the design and delivery of scalable machine learning systems and customer-facing generative AI applications for Rovo Chat. Build agent harnesses that improve quality, reliability, and latency; advance state-of-the-art AI capabilities; lead engineers through technical design and launch; and collaborate cross-functionally with teams and internal customers. The role also involves applying a deep understanding of the machine learning project lifecycle and supporting platform-based product development.

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.

Lead digital customer success programs that drive adoption, retention, and expansion across Atlassian’s global customer base. Coordinate messaging across email, in-app, community, learning, AI agent, and human channels. Partner with Product, Data Science, Marketing, and Customer Success to use telemetry, content, automation, and experimentation to improve customer journeys. Influence cross-functional teams, optimize operating practices, mentor colleagues, and connect initiatives to revenue and customer outcomes.

Own product marketing and developer go-to-market strategy for Atlassian Forge and its broader developer platform. Lead positioning, messaging, launches, adoption, developer growth, community programs, and the TEAM for Builders event. Translate technical capabilities such as APIs, AI workflows, MCP, and agents into compelling developer value. Partner with Product, Engineering, Advocacy, Events, Community, Growth, and Ecosystem teams while managing AWS partner funding and driving developer activation, Forge adoption, and Marketplace outcomes.

As a Principal Machine Learning Systems Engineer, you will lead the development and implementation of machine learning systems and solutions to enhance team collaboration and productivity across Atlassian's software products.

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 paid search strategy across its product portfolio. Owns channel governance, bidding automation, forecasting, budget allocation, experimentation, attribution, agency execution, brand defense, ad messaging, and AI-driven search strategy. Partners with Data Science, Ad Operations, Product Marketing, Web, and executives to improve demand capture, measurement, and portfolio performance.