Senior Software Engineer II AI-Native, Messaging

Life360

2h • $148,000 - $216,500 • Remote

About The Position

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. 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 where AI is integral to the work process, with engineers directing AI agents for various tasks. 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. The stack includes Kafka and Kafka Streams, Spring Boot (Java 21), Go, Protobuf/gRPC, Terraform, and AWS. Life360's mission is to keep people close to the ones they love, empowering members to protect people, pets, and things with services like location sharing, safe driver reports, and crash detection. Life360 serves approximately 91.6 million monthly active users across more than 180 countries and has more than 500 remote-first employees.

Requirements

• 6+ years of backend software engineering experience.
• Strong proficiency with Java and Spring Boot — this is our primary stack and you should be comfortable reading, writing, and debugging it without assistance.
• Production experience with Apache Kafka or a comparable distributed streaming platform (Pulsar, Kinesis, Pub/Sub).
• Solid grounding in distributed systems concepts: consistency, fault tolerance, replication, delivery semantics, and data durability tradeoffs.
• Comfortable with AWS (or equivalent cloud) and containerized deployments on Kubernetes.
• Hands-on experience with Terraform or comparable IaC for real, multi-environment infrastructure.
• Prompting excellence — you give agents context, constraints, and expected outcomes. You don't type "do X" and hope.
• Critical validation — you can explain every line the agent wrote, identify where it succeeded and where it fell short, and correct it. You treat AI output as a draft from a smart junior, not an oracle.
• Agentic decomposition — you break ambiguous problems into discrete, verifiable sub-tasks that an agent can actually execute, rather than handing it a blob of intent.
• Parallel workstreams — you run multiple agents or sessions concurrently on independent tasks instead of hand-holding one at a time.
• Troubleshooting with AI, not around it — when an agent hallucinates an API, suggests a deprecated library, or produces code that compiles but is wrong, you diagnose and recover. The interview is designed so things will go wrong; how you get unstuck is a signal.
• Technical authority — you understand the underlying tech (Kafka internals, Protobuf wire format, JDBC, SASL_SSL auth, partitioning semantics) well enough to catch the agent when it's confidently wrong.
• Problem-solving mindset — you structure ambiguous problems precisely before reaching for a tool, AI or otherwise.
• Ownership — you take a feature from design through production and through the 3 a.m. page that follows.
• Direct, respectful communication — you can explain technical tradeoffs clearly, push back when you disagree, and change your mind when the evidence changes.
• Collaborative — you work well across teams and value perspectives that aren't your own.

Nice To Haves

• Go experience (we use it for CLI tools and some services).
• gRPC and Protocol Buffers in production, including schema evolution strategies.
• Stream processing frameworks — Kafka Streams, Flink (SQL or DataStream).
• Confluent Platform or Confluent Cloud specifics (Schema Registry, Connect, ksqlDB, Stream Governance).
• CI/CD with GitHub Actions, artifact management (Maven, Nexus).
• Observability tooling — DataDog, Prometheus, Grafana.
• Prior experience on large-scale platform migrations or infrastructure modernization programs.
• Building or running evals for LLM-based workflows, prompt libraries, or agent tooling at your current or previous team.

Responsibilities

• 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 scale.
• Mentor teammates, raise the bar on technical standards, and contribute to the team's API design, data contracts, and integration patterns.

Benefits

• Competitive pay and benefits.
• Medical, dental, vision, life and disability insurance plans (100% paid for US employees). Supplemental medical and dental plans for Canadian employees.
• 401(k) plan with company matching program in the US and RRSP with DPSP plan for Canadian employees.
• Employee Assistance Program (EAP) for mental wellness.
• Flexible PTO and 12 company-wide days off throughout the year.
• Learning & Development programs.
• Equipment, tools, and reimbursement support for a productive remote environment.
• Free Life360 Platinum Membership for your preferred circle.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed
• Number of Employees: 101-250 employees