Top AI & Machine Learning Jobs in San Francisco, CA

Leads the design and implementation of AI-powered finance workflows, agents, copilots, and integrations across ERP, billing, procurement, and other financial systems. Establishes AI architecture, governance, security, observability, and responsible-use standards while partnering with Finance, IT, Security, Legal, and Audit. Identifies automation opportunities, measures business impact, builds reusable frameworks, and enables nontechnical teams to adopt AI solutions.

Own the architecture and development of Klaviyo’s enterprise MarTech ecosystem, connecting marketing data, customer journeys, campaign execution, budgeting, and performance insights. Build AI-powered capabilities including generative AI, predictive scoring, and intent automation. Architect integrations across HubSpot, Salesforce, NetSuite, Coupa, and internal systems, advise on technical designs, support vendor evaluations, and partner cross-functionally to improve marketing efficiency and ROI.

Lead the design and build of AI-first People systems, integrating LLMs and enterprise HR SaaS to automate recruiting, onboarding, performance, and other People workflows. Define architecture, standards, responsible-AI practices, and success metrics while partnering cross-functionally to enable copilots, agents, and reusable automation frameworks for People teams.

Design and build AI-powered prototypes and end-to-end LLM workflows using Claude, ChatGPT, and other platforms. Partner with Services leaders to prioritize opportunities, translate needs into roadmaps, integrate AI with internal systems, and help productionize solutions. Create documentation, run enablement sessions, and drive adoption across Global Services while collaborating with centralized AI engineering.

Partner with Customer Success leaders to identify and prioritize AI opportunities; prototype and productize LLM-powered solutions and end-to-end workflows; integrate AI with APIs and internal data sources; develop prompts, skills, agents, and retrieval systems; enable teams via documentation, workshops, and training; and collaborate with engineering to scale prototypes into production.