Senior Backend Engineer II - AI-Native, Devices Cloud

Life360

7h•Remote

About The Position

Life360's mission is to keep people close to the ones they love, offering a category-leading mobile app and Tile tracking devices for location sharing, safe driver reports, and crash detection with emergency dispatch to approximately 91.6 million monthly active users across over 180 countries. Life360 is a Remote First company. The Devices Cloud team owns the backend services for Life360's connected hardware (Tile trackers, Jiobit wearables, and emerging device categories), managing real-time device state, high-frequency telemetry ingest, and cloud infrastructure to translate physical-world signals into reliable, family-facing experiences. This team operates at the intersection of hardware, firmware, mobile, and platform, with a strong emphasis on correctness and reliability at scale. It is an AI-Native team, integrating AI as a first-class collaborator in all development stages, including spec writing, code generation, test authoring, incident triage, and system design, to achieve faster delivery and deeper insights. As a Senior Backend Engineer on this team, you will build and operate cloud services for Life360's connected devices, working at the intersection of hardware, firmware, and mobile to translate real-world device signals into scalable backend systems. The role requires native thinking in AI tools, orchestrating agents for specs, code, tests, verification, and reviews. You will help define and evolve AI-native engineering practices for the Devices Cloud team, creating playbooks for the broader organization, while delivering backend systems that serve millions of families daily.

Requirements

5+ years of experience building and operating high-quality backend services in Java, Go, Python, or similar languages.
Hands-on experience prompting, evaluating, and building with LLMs — not just autocomplete, but as a genuine development partner.
Deep experience with agentic workflows, prompt engineering, context window management, and MCP/function calling.
A track record of using AI tooling to multiply your own output — faster specs, better test coverage, cleaner code, faster debugging.
Strong experience with microservices architecture, RESTful API design, and distributed systems.
Solid skills with cloud infrastructure (AWS preferred), container orchestration, and production deployments.
Experience with databases (relational and/or NoSQL), caching, and event/streaming systems.
Ability to collaborate across teams and articulate technical tradeoffs clearly.
A genuine drive to define what AI-native looks like for complex, hardware-connected systems — you're not waiting for the playbook, you want to write it.
Daily use of AI coding assistants (Claude Code, Cursor, and GitHub Copilot) for real, substantive tasks: analysis, coding, refactoring, testing, navigating codebases, and documentation.
Accountability for everything you ship, including AI-generated code.
Operating with meaningful leverage using AI to do more with the same time, taking on problems that would otherwise require a larger team.
Sharing what works, automating prompting strategies, and helping teammates earlier in their AI workflow adoption get up to speed.
Staying current with tooling changes and bringing recommendations to the team when something would meaningfully improve how we work.

Nice To Haves

Experience with IoT, telematics, or embedded/hardware-adjacent systems.
Familiarity with Kafka, Kinesis, or other high-throughput streaming platforms.
Experience with high-frequency ingest systems and time-series data.
Background with observability tooling (Prometheus, Grafana, OpenTelemetry, DataDog).
Knowledge of SRE practices and automated testing frameworks.

Responsibilities

Design, build, and maintain backend services for device connectivity, telemetry ingest, health monitoring, and command/control operations — using AI (Claude Code) as a first-class collaborator in your daily development workflow.
Use agentic workflows to dramatically increase delivery velocity without sacrificing quality: from generating service scaffolding, to writing and validating test coverage, to triage and root cause analysis during incidents.
Help define and codify AI-native engineering practices for the Devices Cloud team — establishing patterns the broader org can adopt.
Collaborate with firmware, mobile, and product teams to define APIs and workflows for device-driven features.
Build and own data pipelines for high-throughput telemetry streams using Kafka or similar streaming technologies.
Deliver scalable, resilient microservices on AWS (EKS, Lambda, DynamoDB, SQS, etc.).
Instrument services for observability, reliability, and SLO compliance.
Participate in on-call rotation and live incident response.
Write clean, testable, performant code; contribute to CI/CD automation and improve team-wide engineering standards.
Mentor teammates and help evolve the team's AI-native engineering culture.