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 97.8 million monthly active users across more than 180 countries and is a Remote First company with more than 500 employees.
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. The mission is to transform Life360 from the app you open to find your family into an intelligent operating system families rely on daily. The team designs and builds an AI-powered layer on top of our family graph, leveraging core location technologies and a global 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 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, and build the eval harness that tells us whether the system is actually getting better.
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, and document understanding.
- Choose models and hosting; decide what is self-hosted, API-based, fine-tuned, or prompt-engineered.
- Build the serving infrastructure – latency budgets, batching, caching, fallbacks, and graceful degradation.
- Build the eval loop to know whether changes make the product better or worse, not just whether the demo works.
- Own the cost model – track spend per user, per feature, per family.
- Set the bar for safety, privacy, and trust at the infrastructure layer, with monitoring adopted as a first-class citizen.
What We’re Looking For
- Deep engineering experience, with significant time spent building and running AI systems in production at meaningful scale.
- Fluent in modern LLM serving (vLLM, TGI, SGLang, hosted APIs) with the judgment to pick what’s right for the job.
- You think in evals and have built harnesses – you can tell the difference between vibes-based iteration and real progress.
- You can build a cloud system from the ground up and keep the bill in check.
- AI-native in your daily workflow, with hands-on experience in agentic workflows, prompt engineering, context window management, MCP, and function calling.
- Strong written communication and comfort deciding with incomplete information.
Compensation
Salary: $190,000 – $280,000 / year. For candidates based in the US, the salary range for this position is $190,000 to $280,000 USD. The compensation package includes a wide range of medical, dental, vision, financial, and other benefits, as well as equity. Final salary may vary considerably depending on background, experience, and location.
