Job Description
About Life360 Life360’s mission is to keep people close to the ones they love. Our category-leading mobile app,Tile tracking devices, and Pet GPS tracker 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 95.8 million monthly active users (MAU), as of December 31, 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 who are basically 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. We are AI Native We are building an AI native company where AI is an integral part of how we build and operate. AI tool usage during interviews varies by role. You may be asked to demonstrate proficiency with AI tools, discuss how you leverage AI, or complete interview exercises without AI assistance. Your Recruiter will provide clear guidance as you move through the interview process. Undisclosed use of AI not previously discussed with or approved by your Recruiter may impact your candidacy. About The Team The AI Lab is a small, founder-led team operating like a zero-to-one startup inside Life360, building the company’s next chapter. Our mission is to transform Life360 from the app you open to find your family into an intelligent operating system families rely on daily. We design and build an AI-powered layer on top of our family graph, leveraging our core location technologies and global 100 million-user base to proactively surface what matters across location, schedules, and everyday life. About the Job Your role is owning how Life360's deterministic backend (location data, the family graph, behavioral signals, user insights) pairs with the probabilistic world of LLMs. You decide how those two sides talk to each other, and you make sure the seams don't show. You own the inference pipeline end to end: model selection, serving infrastructure, evaluation loops, prompt and context strategy, and the cloud services that keep it all running in production. You make the tradeoffs between latency, cost, and quality. You build the eval harness that tells us whether the system is actually getting better, not just feeling different. If you do this job right, the product feels seamless to users, the data stays safe, and inference costs don't run away from us. You are a cloud engineer who is deeply fluent in how AI systems actually work in production. For candidates based in the US, the salary range for this position is $197,000 to $290,000 USD. For candidates based out of Canada, the salary range for this position is $229,000 to $268,000 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. What You’ll Do Architect the inference pipeline that turns Life360 into a context layer LLMs can reason over: soul files, family context, behavioral signals, document understanding. Choose models. Choose hosting. Decide what is self-hosted, API-based, fine-tuned, or prompt-engineered. Revisit those decisions as the landscape changes. Build the serving infrastructure. Latency budgets, batching, caching, fallbacks, graceful degradation. Make it run at scale before scale gets here. Build the eval loop. Know whether changes make the product better or worse, not just whether the demo works. This is the difference between a real product and a magic trick. Own the cost model. Track spend per user, per feature, per family. Find the levers (context compression, model routing, pre-computation) that keep unit economics working from a test group to millions. Define what gets persisted, what gets summarized, and what gets thrown away. The architecture decision of "what does our data look like to an LLM" lives with you. Set the bar for safety, privacy, and trust at the infrastructure layer. Sensitive categories filtered before they propagate. Encryption, access controls, audit trails built in, not bolted on. What We’re Looking For If there's one trait we need above all others,
