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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Platform Engineer - **Company:** Life360 - **Location:** United States (Remote available) - **Experience:** Experienced - **Salary:** $149,000 - $271,500 - **Contract:** Permanent contract - **Skills:** Microsoft Access, Application Programming Interfaces (APIs), Artificial Intelligence, User Authentication, Software as a Service, Code Generation, Cursor, Distributed Systems, Routing, Data Processing, GitHub Copilot, Delivery Pipeline, Large Language Models, Claude Code, Multi-Agent Systems, AI Coding Agents, Backend, Agentic-AI, AI Platforms, Kubernetes, Infrastructure Automation Frameworks, Low Latency, Machine Learning Operations, Model Inference, Code Restructuring - **Published:** November 11, 2026 - **Apply:** https://job-boards.greenhouse.io/life360/jobs/8936492002 ## About the Role * 8+ years of engineering experience, including 3+ years building platforms or infrastructure used by other engineers * Experience building and operating production AI systems, such as agentic workflows, LLM integrations, retrieval, and evaluation * Working knowledge of MCP and agent orchestration, including how agents connect to internal systems with appropriate controls * Strong backend and cloud fundamentals: distributed systems, APIs, infrastructure as code, and reliability and observability practices * Experience running AI inference in production, including latency, reliability, provider management, and cost tradeoffs * Sound judgment on what deserves a shared platform investment and what doesn't * Clear written communication: design docs and standards precise enough for other engineers (and their agents) to build against, * Experience with non-human identity, service-account, or agent authZ models * Experience building or operating an agent orchestration or deployment platform, or an MCP-style connector gateway * Familiarity with SOC 2, GDPR, or similar compliance frameworks * Background in ML infrastructure or model observability * Experience with consumer-scale products or privacy-sensitive data AI-Native Expectations ## Description The AI Platforms team at Life360 builds the shared foundation that enables the rapid and safe use of AI across the organization. This includes model access and routing, the MCP gateway and connectors, the agent runtime and orchestration platform, memory and knowledge infrastructure, evaluations and observability, as well as cost controls. As an AI Platform Engineer, you will play a crucial role in developing the infrastructure that supports every engineer and agent at Life360, extending it from internal productivity to member-facing product features. In this role, you will own production components end to end, setting the technical direction for your area and transforming early momentum into established pathways that other teams will adopt. You are a builder at heart, responsible for shipping code, reviewing designs, and making pragmatic build-vs-buy decisions as the model and tooling landscape evolves. You will collaborate closely with the AI Dev Tooling and Agentic PDLC teams, which are reshaping how Life360 builds with agents, while also extending the same foundational capabilities to power AI features for our members. You will report to the Director of AI Platforms., * Establish the overarching vision and paved paths that enable teams across Life360 to adopt AI-native engineering best practices * Build the agent runtime and orchestration platform, including identity, authorization, and audit for scheduled and unattended agents * Operate the MCP gateway and connector infrastructure: secure brokering so agents reach internal data, APIs, repos, and SaaS tools through managed, auditable connections rather than unmanaged direct access * Support the LLM gateway: model access and routing, authentication, rate limits, token budgets, and per-team cost attribution * Build eval and regression infrastructure: golden datasets, offline evals, CI hooks, and result storage, with quality regression treated as a first-class alert * Setup observability: prompt and response tracing, cost per request, quality signals, and drift detection * Work with Security, Privacy, and Legal to build data handling, tenant isolation, and auditability into the platform from day one * Define how product teams access, run, and monitor AI inference in production at member scale: serving, latency, reliability, and provider management * Own what you build in production: SLOs, dashboards, deployment pipelines, and incident response * Enable memory, retrieval, and knowledge infrastructure that agents can share * Make the right way the easy way: SDKs, templates, reusable skills, docs, and internal champions that engineers and their agents choose to use * Partner with AI DevX and Agentic PDLC to turn their needs into shared primitives instead of one-off builds * Mentor engineers on agent architecture and evaluation rigor, and write design docs and standards other teams can build against * Explore foundational memory, retrieval, and shared knowledge capabilities for agents as longer-term platform needs evolve, We use AI coding tools as a professional standard on this team. Here's what that means in practice: * Daily use: You use AI coding assistants (we support Claude Code, Cursor, and GitHub Copilot) for real, substantive tasks: analysis, coding, refactoring, testing, navigating codebases, and documentation. Not just research or autocomplete. * Judgment and ownership: AI-generated code gets the same review you'd give any PR. You are accountable for everything you ship. * Velocity: We expect senior engineers who use AI well to operate with meaningful leverage - doing more with the same time, taking on problems that would otherwise require a larger team. * Team leadership: You share what works. You automate prompting strategies that others can build on and help teammates earlier in their AI workflow adoption get up to speed. * Continuous learning: The tooling is changing fast. You stay current and bring recommendations to the team when something would meaningfully improve how we work.