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

Manage Life360’s patent and trademark portfolios, including prosecution, enforcement, and outside counsel relationships. Advise on intellectual property matters involving patents, trademarks, trade secrets, licensing, open-source software, product development, and commercial transactions. Draft and negotiate IP-related agreements, support M&A due diligence, review marketing materials for compliance, and counsel cross-functional teams on strategic IP risks and opportunities.

Leads MAU analytics and data science, managing decision scientists and data scientists while setting quality standards. Owns MAU driver modeling, forecasting, causal analysis, experimentation, and exploratory work supporting growth strategy. Establishes consistent analytical practices, partners with revenue, product, engineering, and data engineering leaders, and embeds the team in roadmap planning. Promotes AI-native workflows through coding agents, automation, and analytical tools while coaching team members into strategic advisors.

Owner of Life360's unified organic discovery strategy across SEO, AEO/GEO, and ASO. Lead content strategy, site information architecture, technical SEO, structured data/AEO optimization, AI citation growth, regional localization, off-page authority, and performance reporting. Manage a content strategist and external agencies and partner with Engineering, Product, Marketing, and PR to execute technical and editorial initiatives that drive organic traffic and conversions.

Design, implement, and own a reusable on-device ML inference platform for resource-constrained devices. Integrate inference into RTOS firmware, develop drivers and DMA paths, debug on real hardware, train and optimize models for microcontroller-class hardware, and ship features to production while raising team expertise and using AI-assisted tooling.

Design, build, and maintain scalable distributed data pipelines and a secure data lakehouse for streaming and batch processing to support real-time analytics, ML, and experimentation. Automate, test, and harden workflows, architect logical and physical data models, build ML model features, and collaborate with product, analytics, and data science teams to turn data into value.