Staff Engineer, Applied AI

StudyFetch•Beverly Hills, CA

3d•$170,000 - $270,000•Onsite

About The Position

We are a technology company building AI-native learning products used by more than seven million students worldwide, alongside Honen, our workforce-learning platform for organizations. Both run on the Learn Engine, the intelligence that moves a learner from initial understanding to demonstrated mastery. We work with partners like NVIDIA to bring responsible, learning-first AI to the students who need it most. This is a founding-team role. You'll work directly with the people making the decisions, own meaningful parts of the product, and ship AI features that reach millions of learners, not prototypes that stall at the demo stage.

Requirements

7+ years building and shipping production software, with real depth in fullstack and applied AI, OR You were a founding or early engineer who owned a significant share of a real product. Fewer years on paper, but you took something from nothing to live users and can demonstrate it.
Shipped AI to production with real users, not demos. You can walk us through a feature you built, what it did, which parts were yours, and what went wrong.
Use AI every day and have informed opinions about it.
Worked through launch crunch and know how you stay effective under pressure.
Candid about tradeoffs. You can tell us what surprised you last time and what you'd do differently.
The mission is why you're here. What sustains you through the hard weeks is the learner on the other end, the one who finally understands because of something you built.

Nice To Haves

Deep in most of the following stack items and able to ramp quickly on the rest: Frontend: Next.js, React, TypeScript, Tailwind; Backend: Node.js, Express, REST API design, serverless, k8s; AI/LLM: LLM application development, prompt engineering, evals, RAG, agent workflows, vllm; Data: MongoDB and PostgreSQL; working knowledge of vector databases; Cloud: AWS (Bedrock, Lambda, S3, VPC), Vercel; Mobile: React Native (iOS/Android).

Responsibilities

Whole product surfaces, end to end. The decision to build, the architecture, production, and the on-call that follows. You own the outcome.
AI and LLM features in the live product. Prompt and eval pipelines, retrieval, agent workflows, and the demanding work of keeping latency, cost, and model behavior reliable for real users at scale.
Full-stack work across web and mobile. You decide where the line between client, server, and model belongs, and you stand behind it.
The bar for the team. The patterns you set are the ones the rest of the team follows.