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

Build and operate production-grade agentic AI systems, owning agent evaluation, testing, retrieval, orchestration, monitoring, and production reliability. Design workflows with LangChain or LangGraph, create evaluation harnesses and regression gates, diagnose failures, optimize latency and cost, and establish technical standards. The role requires 6+ years of software engineering experience, including 2+ years building production LLM or agent systems, plus strong ownership and communication in an ambiguous startup environment.

Own Hilbert’s marketing channels and growth execution, including LinkedIn, founder communications, blog, newsletter, SEO/AEO, videocast distribution, events, community, landing pages, lifecycle nurture, and conversion measurement. Build channels from zero, repurpose customer evidence into content, test new distribution opportunities, create AI-enabled systems, and measure account engagement and qualified meetings rather than vanity metrics.

Lead and build Hilbert's end-to-end privacy program embedded in product and engineering; ensure compliance with US, UK, and EU privacy laws; negotiate enterprise DPAs and security addenda; advise on privacy-by-design for agentic AI features; manage incident response and customer legal/security engagement.

Lead customer integrations by building warehouse-native or managed ClickHouse pipelines, construct semantic metadata layers, canonicalize source data into unified growth models, and act as the technical lead for enterprise deployments while partnering with AI discovery agents to accelerate onboarding and mapping.

First marketing hire responsible for positioning, messaging, GTM strategy, product launches, content (podcast, thought leadership, case studies), events, branding, measurement, and building a marketing team and repeatable systems working closely with founders and GTM leaders.

Design, build, and maintain production-grade AI systems and agent-based workflows; own end-to-end pipelines from experimentation to deployment and monitoring; build evaluation pipelines; collaborate with product, data, and GTM; iterate quickly under ambiguity to shape the AI stack.