Senior Software Engineer II AI-Native, Messaging

About Life360

Life360's mission is to keep people close to the ones they love. Our category-leading mobile app and Tile tracking devices empower members to protect the people, pets, and things they care about most with a range of services, including location sharing, safe driver reports, and crash detection with emergency dispatch. Life360 serves approximately 91.6 million monthly active users (MAU), as of September 30, 2025, across more than 180 countries.

Life360 delivers peace of mind and enhances everyday family life with seamless coordination for all the moments that matter, big and small. By continuing to innovate and deliver for our customers, we have become a household name and the must-have mobile-based membership for families (and those friends that basically are family).

Life360 has more than 500 (and growing!) remote-first employees. For more information, please visit life360.com.

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.

About the Team

The Messaging team owns the backbone of Life360's real-time infrastructure — the event streaming and data pipelines that keep millions of families connected. We process billions of events every day across location updates, safety alerts, and the push notifications that make the product feel instant.

We are a small, platform-focused, AI-native team. AI isn't a productivity add-on here — it's how we work. Every engineer on the team directs AI agents as a core part of their daily loop: scaffolding services, drafting Terraform, exploring unfamiliar codebases, writing migration scripts, and reviewing diffs. We move faster and go deeper on hard distributed systems problems because we've built the habits, prompts, and guardrails to make agentic work reliable.

Current initiatives include the MSK-to-Confluent Cloud migration, organization-wide schema registry and Protobuf governance, and the next generation of Life360's streaming services. Our stack is Kafka and Kafka Streams, Spring Boot (Java 21), Go, Protobuf/gRPC, Terraform, and AWS.

About the Job

This is a hands-on senior engineering role for someone who has already made AI a first-class collaborator in their work. You'll design streaming services, write and review code, own production incidents, and ship infrastructure — and you'll do all of it with agents running in parallel alongside you. We're looking for an engineer who already decomposes work into agentic workflows, critiques agent output with real technical authority, and ships production code faster because of it.

You'll help shape the team's technical direction on Kafka-first streaming infrastructure and the next phase of our high-availability roadmap. You'll also help evolve how the team itself works with AI — the prompts, the evals, the review patterns, the escape hatches when agents go sideways.

What You'll Do

• Design, build, and operate streaming services on Kafka, Spring Boot, and Spring Cloud Stream — directing agents to scaffold, test, and iterate, and owning the outcome end-to-end.
• Develop and manage Kafka connectors for data integration (DynamoDB, S3, NSQ, custom sinks/sources) and the SMT chains that keep them honest.
• Own schema management and evolution across Protobuf, Schema Registry, and multi-language code generation — including the Gradle/Nexus publishing pipelines that back it.
• Drive platform migrations (MSK → Confluent Cloud, NSQ → Kafka) including dual-cluster consumer patterns, VPC peering, and cutover playbooks.
• Build monitoring, alerting, and operational tooling (DataDog, PagerDuty, Prometheus) that catch problems before pages fire.
• Write infrastructure as code in Terraform, ship it through CI/CD, and participate in the on-call rotation and incident response for the services you own.
• Work AI-natively as the default mode of operation. Run multiple agents in parallel. Write prompts with real context and constraints. Review every diff like you wrote it yourself. Know when to throw the agent's output out and do it by hand.
• Evolve the team's AI-native practices — prompt libraries, evals, review rituals, and the guardrails that make all of it safe at production s