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

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 and Canada) regardless of any specified location above.

The Experimentation & Machine Learning Intelligence team at Life360 sits within the Growth org and builds the platforms and services that let the company learn and personalize at scale. We own the experimentation platform (A/B testing, feature flagging, metrics and analysis) and the infrastructure behind our recommendation and personalization systems — the data pipelines, feature stores, and online serving paths that turn signals into better member experiences.

Our work helps every product team ship with confidence and measure real impact for the families who rely on Life360.

We are a product-minded, AI-Native engineering team. That means AI isn’t just a tool we use — it’s how we work. We’ve redesigned our team workflows around AI, treating it as a first-class collaborator at every stage: ideation, coding, testing, review, and iteration.

We ship faster and go deeper because of it, and we’re looking for someone who wants to help define what that looks like at scale.

Life360 is hiring a backend AI-Native Senior Software Engineer II — a senior engineer who doesn’t just use AI tools, but thinks natively in them. You'll be central to how the team builds its experimentation and ML infrastructure, develops its services, and how we use AI for development in the future.

You'll design and build the systems that power experimentation and recommendation at Life360 — high-throughput data pipelines, low-latency serving services, feature infrastructure, and the alerting and monitoring that keep them reliable. Our serving infrastructure runs at consumer internet scale, where milliseconds matter and an experimental misconfiguration can affect the experience of millions of families within hours.

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 publish AI-native backend engineering playbooks the broader eng org adopts.

In year one, success looks like having shipped meaningful improvements to the experimentation platform, established agentic workflow playbooks the broader engineering org can adopt, and operating with demonstrably higher velocity than a non-AI-native engineer in a comparable role.

For candidates based in the US, the salary range for this position is $118,500 to $216,500 USD. For candidates based out of Canada, the salary range for this position is $171,500 to $201,000 CAD. Note: Please be aware that the job title for positions in Canada will be "Developer" in lieu of "Engineer." We take into consideration an individual's background and experience in determining final salary - therefore, base pay offered may vary considerably depending on geographic location, job-related knowledge, skills, and experience.

The compensation package includes a wide range of medical, dental, vision, financial, and other benefits, as well as equity.

• 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.

• 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.

• 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

• We believe culture fit and problem-solving ability matter more than checking every technical box. We're happy to help you grow into areas where you have less experience.

• 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

• Understanding of CI/CD patterns, GitHub Actions, and artifact management (Maven, Nexus)

• Experience with observability tooling (Prometheus, Grafana, DataDog)

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 the team when something would meaningfully improve how we work.

Our company’s mission driven culture is guided by our shared values to create a trusted work environment where you can bring your authentic self to work and make a positive difference