Principal Machine Learning Engineer

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As a Principal Machine Learning Engineer in the Confluence AI organization, you will help shape the next generation of AI-powered experiences across Confluence. This is a hands-on principal-level role for an engineer who can turn fast-moving advances in machine learning into high-quality product experiences that reach customers at scale.

You will work across product, platform, and partner teams to define technical direction, solve ambiguous problems, and deliver AI capabilities that improve how users create, edit, discover, and interact with content. The role spans the full stack of AI product development, from model-informed product design and evaluation strategy to production engineering, experimentation, and long-term technical leadership.

This role is especially well suited for someone who combines strong machine learning depth with product instinct and systems thinking. Success in this position means not only building sophisticated AI systems, but also identifying the highest-value opportunities, raising engineering standards, and helping multiple teams move faster and with more confidence.

Set technical direction for machine learning and AI initiatives that power Confluence experiences across areas such as content creation, editing, summarization, recommendations, and multimodal interaction.

Design, build, and evolve production-grade ML systems, evaluation workflows, experimentation loops, and supporting infrastructure that enable reliable delivery of AI features at scale.

Lead the development of customer-facing AI capabilities by translating ambiguous product opportunities into practical technical strategies, measurable outcomes, and shipped experiences.

Drive advances in areas such as model behavior, retrieval, ranking, prompt and workflow design, automated evaluation, quality measurement, and system reliability.

Partner closely with engineering, product, design, analytics, and platform teams to align on priorities, influence roadmaps, and deliver cross-organizational initiatives.

Identify systemic product or model failure modes, develop interventions, and close the loop between technical improvements and customer impact through experimentation and data-driven iteration.