Staff AI Platform Engineer
Life360 · Remote, USA
About this role
**About Life360** Life360’s mission is to keep people close to the ones they love. We build a category-leading mobile app, Tile tracking devices, and a Pet GPS tracker—helping members protect the people, pets, and things they care about most with services like location sharing, safe driver reports, and crash detection with emergency dispatch. Life360 serves ~107.7M monthly active users (MAU) across 180+ countries. Life360 is **remote-first** (unless otherwise specified, roles can be performed remotely within the US). **About the Team (AI Platforms)** The AI Platforms team builds the shared foundation that lets Life360 use AI quickly and safely, including: - Model access & routing - MCP gateway & connectors - Agent runtime & orchestration - Memory & knowledge infrastructure - Evals & observability - Cost controls **About the Job** As a **Staff AI Platform Engineer**, you’ll develop the infrastructure that supports every engineer and agent at Life360—expanding from internal productivity to member-facing AI features. You’ll own production components end-to-end, set technical direction, and turn early momentum into pathways other teams can adopt. You’ll collaborate closely with **AI Dev Tooling** and **Agentic PDLC** teams, and report to the **Director of AI Platforms**. **What You’ll Do** - Establish the vision and “paved paths” for AI-native engineering best practices across Life360 - Build the agent runtime & orchestration platform (including identity, authorization, and audit for scheduled/unattended agents) - Operate the MCP gateway and connector infrastructure with secure, managed, auditable access - Support the LLM gateway (model access/routing, auth, rate limits, token budgets, cost attribution) - Build eval & regression infrastructure (golden datasets, offline evals, CI hooks, result storage) - Set up observability (prompt/response tracing, cost per request, quality signals, drift detection) - Partner with Security/Privacy/Legal on data handling, tenant isolation, and auditability - Define how product teams access, run, and monitor AI inference at member scale (serving, latency, reliability, provider management) - Own production outcomes: SLOs, dashboards, deployment pipelines, incident response - Enable shared memory, retrieval, and knowledge infrastructure for agents - Make the right way the easy way (SDKs, templates, reusable skills, docs, internal champions) - Partner with AI DevX & Agentic PDLC to turn needs into shared primitives - Mentor engineers on agent architecture and evaluation rigor; write standards/design docs - Explore longer-term foundational memory/retrieval/shared knowledge capabilities **What We’re Looking For** - **8+ years** engineering experience, including **3+ years** building platforms/infrastructure used by other engineers - Experience building & operating production AI systems (agentic workflows, LLM integrations, retrieval, evaluation) - Working knowledge of MCP and agent orchestration (controls for connecting agents to internal systems) - Strong backend/cloud fundamentals (distributed systems, APIs, IaC, reliability & observability) - Experience running AI inference in production (latency, reliability, provider management, cost tradeoffs) - Sound judgment on what should be a shared platform investment - Clear written communication (design docs/standards other engineers can build against) **Bonus Experience** - Non-human identity / service-account / agent authZ models - Agent orchestration or dep
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