Top Tech Jobs & Startup Jobs in Toronto

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.

Own analysis and algorithmic solutions for ads delivery; train, deploy, and scale ML/optimization models into production; build logging, monitoring, and lineage; improve data pipelines; mentor data scientists; use agentic AI tools daily; collaborate with engineers to integrate models into ads systems and handle on-call production incidents.

Build and operate high-throughput experimentation and recommendation infrastructure. Design APIs and services, use agentic AI workflows (LLMs) for development, ensure performance and reliability, participate in on-call, mentor teammates, and codify AI-native engineering practices.

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.

Embed with product, engineering, marketing, and finance teams to own analytical narratives, establish causality via experimentation and causal inference, define measurement strategies, drive actionable insights, shape AI-native analytics tooling, and mentor senior analysts. Focus is on decisions and measurable impact rather than dashboards.

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.