Staff Backend Engineer (AI Native), Family AI Lab

Life360 is a company focused on keeping families connected through innovative technology. They are seeking a Staff Backend Engineer for their AI Lab who will own the inference pipeline, integrating location data and behavioral signals with AI systems to enhance user experience and product functionality.

Responsibilities:

• 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

Requirements:

• 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 — whatever's right for the job)
• You think in evals. You've built harnesses
• You can build a cloud system from the ground up
• Strong opinions about cost
• AI-native in your daily workflow
• Comfortable deciding with incomplete information
• Strong written communication
• Experience with agent systems, retrieval pipelines, or long-context personalization
• Privacy architecture experience in multi-user systems where context is shared across people with different rights to see it
• You've built something where the data itself was the moat
• Self-starter energy
• Ability to operate without a spec
• Experience shipping products every day
• Desire to push for more and demand excellence
• Ability to hold strong opinions and change them when the evidence moves