Senior Software Engineer II (Cloud), AI Native, Experimentation & ML

Life360 is a remote-first company whose mission is to keep people close to the ones they love through location sharing, tracking, safety, and family-oriented services. The Senior Software Engineer II will design and build experimentation and recommendation infrastructure, including APIs, data pipelines, serving services, feature infrastructure, monitoring, and AI-native engineering workflows. The role also involves collaboration, mentoring, architectural contributions, on-call participation, and improving performance and reliability at consumer internet scale.

Responsibilities:

• Design and build high-quality APIs and services for our experimentation platform and recommendation/personalization systems — experiences that are reliable, performant, and genuinely useful to product teams and members
• 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 team, establishing playbooks the broader org can adopt
• Build across the backend stack as needed — shipping polished, performant, and reliable experiences to tens of millions of users
• Collaborate closely with product managers and data teams 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

Requirements:

• 6+ years of backend software engineering experience
• Strong proficiency with Java and Spring Boot (this is 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
• At least 1 year of hands-on experience prompting, evaluating, and building with LLMs
• Growth mindset - You structure problems precisely, you learn quickly, you can show interest outside “your lane”, and you 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 (Claude Code or equivalent) as a genuine development partner: delegating discrete tasks, reviewing outputs critically, and running parallel workstreams rather than hand-holding one agent at a time
• Thoughtful communication - You can explain technical tradeoffs and articulate ideas effectively
• 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
• Experience building or operating experimentation, A/B testing platforms or feature-flagging systems
• Experience supporting recommendation, personalization, or ML-serving systems (feature stores, online/offline pipelines, low-latency model serving)
• Background in stream processing frameworks (Kafka Streams, Flink)
• Experience with schema registries and schema evolution strategies
• Knowledge of Confluent Platform or Confluent Cloud
• Understanding of CI/CD patterns, GitHub Actions, and artifact management (Maven, Nexus)
• Experience with observability tooling (Prometheus, Grafana, DataDog)
• Previous work on ecosystems or integration tooling