Top AI & Machine Learning Jobs in San Francisco, CA

Lead Atlassian’s Agent Platform engineering team, combining people management with hands-on technical leadership. Set direction for applied ML, agent workflows, retrieval, evaluation, observability, reliability, security, privacy, and scalable platform foundations. Hire, coach, and develop engineers; establish planning and execution practices; write production code; and align roadmaps across product, engineering, ML, data, and platform stakeholders to deliver reliable AI experiences.

Lead the vision, strategy, and roadmap for AI experiences across Atlassian’s core products. Partner with engineering, design, data science, platform, and product teams to deliver intuitive AI features, accelerate adoption, define success metrics, gather customer feedback, and iterate based on data. Serve as an AI product leader, mentor stakeholders, communicate with executives, and drive innovation across a global software portfolio.

Design, develop, train, optimize, and deploy machine learning models, including NER, personalization, predictive, and LLM fine-tuning systems. Collaborate with engineering, product, and analytics teams to integrate AI features into Atlassian products. Conduct experimentation, evaluate model performance, monitor product metrics, and improve scalability and reliability. Contribute to foundational model development and advanced AI initiatives while communicating findings to senior leaders.

Build and lead production machine learning and evaluation systems for Atlassian’s Rovo AI agents. Responsibilities include developing datasets, simulations, automated judges, experimentation platforms, and quality signals; advancing reasoning, planning, retrieval, memory, and tool use; identifying and correcting systemic failures; and balancing quality, safety, latency, reliability, and cost. The role requires hands-on technical leadership, cross-functional influence, architectural decision-making, mentorship, and delivery of measurable customer outcomes at scale.

Summer 2027 PhD research internship at Atlassian focused on AI research. The role supports distributed-first collaboration, with virtual interviews and flexibility to work remotely or from a company office.

Conduct AI research as a 2027 graduate hire at Atlassian, collaborating remotely or from company offices. The provided description contains no specific research area, responsibilities, qualifications, or technical requirements.

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.

Architect and ship large-scale ML and evaluation systems for Atlassian Rovo Chat. Define technical strategy for measuring helpfulness, correctness, reliability, and agentic capabilities; develop datasets, simulations, automated judges, experimentation platforms, and production quality signals. Identify failure modes, implement interventions, and measure customer impact. Lead cross-functional initiatives, establish reusable AI quality standards, and mentor engineers while balancing quality, safety, latency, reliability, and cost.

Develop and deploy scalable machine-learning solutions, particularly deep-learning forecasting models for company top-line metrics. Lead the full ML lifecycle from problem definition and experimentation through production deployment and improvement. Design production-grade architectures, evaluate and improve models, collaborate with cross-functional teams, explain technical concepts to varied audiences, and mentor junior engineers.

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.