Senior Software Engineer II AI-Native, backend

About the Job

Life
360 is hiring an AI-Native Backend Focused Engineer — a senior engineer who doesn’t just use AI tools, but thinks natively in them. You’ll be central to how the Growth team builds its products, develops its services, and how we use AI for development in the future.

You’ll design and build features and systems to help grow Life
360’s payments infrastructure, product integrations, alerting and monitoring, everything.

You’ll build and evolve our modern AI-Native engineering workflows — from how we write code, to how we run standups, to how we think about team velocity. You’ll evolve the standard others follow.

For candidates based in the US, the salary range for this position is $148,000 to $216,000 USD. For candidates based out of Canada, the salary range for this position is 171,500 to 201,000 CAD.

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 Git Hub 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.

• Design and build high-quality API and services for our subscribers and payment infrastructure— experiences that are safe, intuitive, and genuinely useful.
• Work with AI (Claude Code) as a first-class collaborator — your primary workflow involves orchestrating agents to create specs, generate code and tests, verify results, and perform reviews.
• Help define and codify AI-Native engineering practices for the Growth team, establishing playbooks the broader org can adopt.
• Build across the full stack as needed — shipping polished, performant, and reliable experiences to tens of millions of users.
• Collaborate closely with product managers and designers to turn complex user problems into elegant, scalable engineering solutions.
• Contribute to architectural decisions, code reviews, and a culture of craft and continuous improvement.
• Participate in on-call rotation and incident response.
• Use agentic workflows to dramatically increase the delivery of strong outcomes — moving faster without sacrificing quality.
• Mentor team members and contribute to team processes, technical standards, and help evolve the team's AI-native engineering practices.
• Support performance, reliability, and accessibility across the features you own.

• 6+ years of backend software engineering experience
• Strong proficiency with Java and Spring Boot (our primary stack)
• Experience with Apache Kafka or similar distributed streaming platforms
• Solid understanding of distributed systems concepts: consistency, fault tolerance, replication and data durability
• Comfortable with cloud infrastructure (AWS preferred) and containerized deployments
• Heavy user of agentic workflows, understands research-plan-implement cycle but doesn’t outsource thinking to agents

• Growth mindset – you structure problems precisely, learn quickly, show interest outside “your lane” and are always happy to try something new.
• Collaborative approach – you communicate clearly, work well across teams, and value diverse perspectives.
• Ownership mentality – you take responsibility for your work from design through production and beyond.
• AI-native working style – you use AI tooling as a genuine development partner: delegating discrete tasks, reviewing outputs critically, and running parallel work streams rather than hand‑holding one…