English Textbooks, Reimaginedby AI Agents
LingoLift (Story Factory) × LingoForest (School OS) — an AI education agent that turns English textbooks into readable, listenable, chatable stories and closes the loop on how students learn. A 4-person team scaling to 8–10 in the next 3 months.
One Closed Loop
A content factory and a school OS, wired together into a single learning ecosystem.
LingoLift — Story Factory
Feed an English textbook to AI agents and get a complete story experience out: graded outlines → chapter stories → stylized illustrations → consistent characters → CosyVoice voiceover → WeChat review gate.
- •8 stable production workflows
- •Multi-modal content generation
- •Quality validation and review
LingoForest — School OS
After students read, Elo/Ability scoring and CAT placement kick in. The model recommends the next book, teachers see learning analytics, and an AI companion chats with students.
- •Learning analytics with 3 models
- •CAT admission and leveling
- •AI companion chat integration
Our Technology Stack
An all-Go backend with a ByteDance Eino agent layer on top of Alibaba models.
Not Starting From Zero
The architecture is built and running at a working prototype. Here's what already works.
8 Stable SFE Workflows
Outlines, chapters, quizzes, illustrations, voiceovers, characters, and the full pipeline — all producing reliably.
Verified Output Quality
3,000-word chapters with illustration quality reviewed and accepted as better than raw LLM output.
4-Identity RBAC
Role-based routing for user / admin / teacher / editor / student is already cut.
Learning Analytics Triple
Session Elo, attitude_score, ability_score, and level_code, with Redis NX 90s sliding-window rate limiting.
3 Hard Constraints Written Into Spec
What's Left
This is why this hiring page exists. The hard engineering is still ahead.
SFE Polishing
- •Dual-mode chapter merge (batch / per-chapter pause)
- •Character visual consistency across chapters
- •Skill registry
- •WeChat review gate
SGS Progression
- •P3 student insights
- •P4 full learning analytics
- •P5 platform: 5s group aggregation, TeacherAsk
Frontend Gaps
- •Student AI companion chat tab not yet wired
- •Teacher group insights still templated
- •SGS P4–P5 frontend sync
Production & Compliance
- •Single-node to cluster (MySQL / MongoDB / Redis)
- •Monitoring and alerting
- •WeChat ecosystem integration
Hiring Priorities
First an AI Agent engineer and two Go backends to push SFE/SGS to P4. Then two frontend engineers to align all three clients. DevOps and QA to finish.
AI Agent Engineer (Senior)
Backend Engineer (SFE, Go)
Backend Engineer (SGS, Go)
Frontend Engineer (Teacher/Editor)
Frontend Engineer (Student)
DevOps / SRE
Benefits & Culture
A small team that builds things that matter — with the freedom to work the way you want.
Remote Friendly
1–2 monthly meetups in Guangzhou (Panyu / Tianhe), primarily remote
Equity Options
Every role gets equity (0.1%–0.5% early-stage, by role and start date)
13-Month Salary
Competitive base with year-end bonus tied to company performance and role KPI
AI-First Workflow
Encouraged to use AI tools — Cursor, Claude, our own products
No Timesheets
No time tracking, no weekly reports; async collaboration via Linear + Feishu
Learning Budget
¥5,000 per person per year for technical / product learning
Flexible Holidays
10 days annual leave on joining, plus extra Spring Festival leave
Product Pride
Not ordinary SaaS — an AI content factory producing craftsmanship
Hiring Process
Four steps, offer within 48 hours of the final interview.
Initial Screening
HR or founder reviews resume + GitHub / portfolio, 30-minute conversation
Technical Interviews
2 rounds, 60 minutes each — first on tech stack fundamentals, second a project deep-dive
CTO + Founder Interview
30-minute conversation about product vision and cultural fit
Offer
Offer extended within 48 hours
A Closed Loop Nobody HasWalked Yet
Five codebases, three learning models, and eight workflows are already built. Come run the loop with us.