Staff AI Platform Engineer

About Life360

Life360’s mission is to keep people close to the ones they love. Our category-leading mobile app, Tile tracking devices, and Pet GPS tracker empower members to protect the people, pets, and things they care about most with a range of services, including location sharing, safe driver reports, and crash detection with emergency dispatch. Life360 serves approximately 107.7 million monthly active users (MAU), as of September 30, 2026, across more than 180 countries.

Life360 delivers peace of mind and enhances everyday family life with seamless coordination for all the moments that matter, big and small. By continuing to innovate and deliver for our customers, we have become a household name and the must-have mobile-based membership for families (and those friends who are basically family).

Life360 has more than 500 (and growing!) remote-first employees. For more information, please visit life360.com .

Life360 is a Remote-First company, which means a remote work environment will be the primary experience for all employees. All positions, unless otherwise specified, can be performed remotely (within the US) regardless of any specified location above.

We are AI Native

We are building an AI native company where AI is an integral part of how we build and operate. AI tool usage during interviews varies by role. You may be asked to demonstrate proficiency with AI tools, discuss how you leverage AI, or complete interview exercises without AI assistance. Your Recruiter will provide clear guidance as you move through the interview process.

Undisclosed use of AI not previously discussed with or approved by your Recruiter may impact your candidacy.

About The Team

The AI Platforms team builds the shared foundation that lets Life360 use AI quickly and safely: model access and routing, MCP gateway and connectors, the agent runtime and orchestration platform, memory and knowledge infrastructure, evals and observability, and cost controls. We work alongside AI Dev Tooling and Agentic PDLC, the teams reshaping how Life360 builds with agents, and we're extending the same foundations to power AI features for our members.

About the Job

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.

The US-based salary range for this position is $149,000 to $271,500 USD. We take into consideration an individual's background, job-related knowledge, skills, and experience in determining final salary. Base pay may also vary based on geographic location, with US work locations falling into one of three geographic tiers.

Total compensation is inclusive of equity compensation in the form of Restricted Stock Units (RSUs) , as well as a comprehensive benefits package, including medical, dental, vision, financial, and other benefits.

What You’ll Do

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

What We’re Looking For

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

Bonus Experience

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

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