About the Devices Cloud Team
The Devices Cloud team owns the backend services that power Life360's fleet of connected hardware — Tile trackers, Jiobit wearables, and emerging device categories. We manage real-time device state, high-frequency telemetry ingest, and the cloud infrastructure that translates physical-world signals into reliable, family-facing experiences. Our work sits at the intersection of hardware, firmware, mobile, and platform — and runs at a scale where correctness and reliability aren't optional.
We are an AI-Native team. AI isn't an add-on to how we work — it's how we work. We've redesigned our development workflows around AI, using it as a first-class collaborator at every stage: spec writing, code generation, test authoring, incident triage, and system design. We ship faster and go deeper because of it. We're looking for engineers who want to help define what AI-native backend development looks like for complex, hardware-connected systems.
About the Job
As a Senior Backend Engineer on the Devices Cloud team, you'll build and operate the cloud services that power Life360's connected devices. This role sits at the intersection of hardware, firmware, and mobile — translating real-world device signals into reliable, scalable backend systems.
You don't just use AI tools. You think natively in them. Your primary workflow involves orchestrating agents to create specs, generate code and tests, verify results, and perform reviews. You'll help establish and evolve AI-native engineering practices for the Devices Cloud team — playbooks the broader org can adopt — while delivering backend systems that serve millions of families daily.
What You'll Do
- 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.
What We're Looking For
- 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.
Nice-to-Have
- 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.
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.