AI Engineer

About the role

As our AI Engineer, you'll keep the AI infrastructure our products and teams run on fast, efficient, and reliable, and you'll build with it. You'll run and optimize our self-hosted inference stack on our own GPU hardware, build the internal platform our employees work through, and ship user-facing agents inside the apps. Your work spans OurFamilyWizard, Cozi, and FamilyWall, and the platforms that power how we build.

Responsibilities

• Run and optimize our self-hosted inference stack
• Run the inference serving layer on our own GPU hardware: choose and tune the serving stack (vLLM, SGLang, TensorRT-LLM) for high throughput and low latency
• Optimize aggressively: tensor parallelism, quantization (FP8, AWQ, GPTQ), KV-cache and prefix caching, continuous batching, speculative decoding, concurrency tuning
• Serve multiple models and features off shared hardware: multi-LoRA, routing, and request scheduling that balances internal workloads against latency-sensitive product traffic
• Keep our AI fast, efficient, and observable
• Make our AI workloads efficient: improve latency, throughput, and GPU utilization so we get the most out of what we run
• Build the visibility: instrument performance and usage across our AI surfaces so there's clear data on how everything is running
• Surface the technical tradeoffs (performance, latency, efficiency) so the people making the calls have what they need to make them
• Build AI features and proactive agents
• Ship the in-app agent layer that helps families coordinate: proactive nudges, smart suggestions, agents that summarize, draft, schedule, and act for busy parents
• Build the substrate underneath: tools, memory, orchestration, guardrails, and evaluation harnesses, integrated cleanly with production APIs alongside our architecture team
• Work in nimble pairs with feature owners, standing up whatever's needed to test an idea, including a vibe-coded UI when that's the fastest path to a real customer

Requirements

5+ years shipping production software, including meaningful applied AI or ML work

Demonstrated experience running and optimizing self-hosted LLMs on dedicated multi-GPU hardware: a serving stack (vLLM, SGLang, or TensorRT-LLM) and the optimization that comes with it (tensor parallelism, quantization, batching, KV cache)

A track record of optimizing inference performance and efficiency (latency, throughput, GPU utilization)

Strong Python and engineering fundamentals, with the full-stack range to stand up a quick UI, and the genuine desire to work app-layer features and not only infra

Hands-on with agent frameworks (Claude Agent SDK, LangGraph, or similar), LLM APIs, embeddings, and RAG

Comfortable with AWS and the devops this role owns: Docker, CI/CD, monitoring, and observability

Experience building internal tooling or platforms others depend on. Bonus for Slack apps, MCP, or agent orchestration at team scale