Jobs at Life360

Design and run high-volume experiments across subscription and monetization experiences, including pricing, trial conversion, SKU routing, churn prevention, and win-back. Translate ambiguous briefs into testable hypotheses, collaborate with product, engineering, data science, and content teams, define success metrics, interpret results, and present learnings. The role requires strong visual and interaction design craft, animation skills, organized Figma files, proactive ideation, and effective use of AI and experimentation tools.

Own and scale Life360’s programmatic advertising exchange and SSP capabilities. Responsibilities include exchange operations, reliability, roadmap and backlog management, monetization optimization, OpenRTB and header-bidding integrations, partner management, ad quality, incident resolution, and cross-functional alignment across Engineering, Sales, Marketing, Finance, Legal, and Business Development.

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.

Lead strategy and execution to grow Life360 subscriptions by defining membership architecture, pricing and packaging, partner monetization, freemium feature roadmaps, experimentation, and AI-native product practices. Drive revenue-per-member, partner integrations, and cross-functional delivery while advocating subscriber value and satisfaction.

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.

Partner with product, engineering, marketing, and finance to drive decisions using causal inference, experimentation, and analytics. Own measurement strategy, present actionable insights, shape AI-native analytics tooling, and ensure analyses translate into product and business outcomes.