First Principle Engineer (FPE) – AI Systems & Workflow Architecture

Shenzhen · Full-time (China-based employment) · Founding Team · Competitive Compensation

About Us
We are an international AI automation company headquartered in North America, with engineering teams in Vancouver, Seoul, Manila — and now building our Shenzhen hub.

Our core product is the Company Brain:
an enterprise-grade workflow & decision automation platform built on structured data pipelines and LLM API–driven agents.

We don’t train or fine-tune models.
We design systems that think, orchestrate, and act.

What Is an FPE (First Principle Engineer)?
This is not a title for seniority alone.
A First Principle Engineer is someone who:
 - Thinks from first principles, not frameworks
 - Turns vague business problems into clear system architectures
 - Designs systems before writing code
 - Balances speed, correctness, and long-term leverage
 - Makes sound decisions when specs are incomplete
 - Owns outcomes, not just tasks
You will operate at the intersection of product, architecture, AI workflows, and execution.

Why This Role Exists
As we scale the Company Brain, we need engineers who can:
 - Define what should be built before asking how to build it
 - Prevent complexity before it appears
 - Design systems that remain simple under real-world pressure
 - Act as force multipliers for the entire engineering team
This role exists to amplify the effectiveness of everyone else.

Role Overview
As a First Principle Engineer, you will take ownership of core system architecture and decision-making across our AI workflow platform.
You will work closely with:
 - Backend engineers building LLM-powered services
 - Frontend engineers designing product experiences
 - UX designers shaping internal tools
 - Business stakeholders defining real operational needs
You are expected to reason holistically and execute selectively.

Key Responsibilities
 - Translate ambiguous business problems into concrete system designs.
 - Define end-to-end architecture for AI-driven workflows (data → agent → decision → action).
 - Design service boundaries, data models, and integration strategies.
 - Decide what not to build and when to simplify.
 - Guide backend and frontend engineers on architecture and trade-offs.
 - Own one or more critical subsystems (e.g. scheduling engine, scoring pipeline, orchestration layer).
 - Evaluate where LLMs should be used — and where deterministic logic is better.
 - Establish engineering principles that prioritize leverage, clarity, and velocity.
 - Document architectural decisions clearly and concisely in English.

What Success Looks Like
 - The system remains understandable as features scale.
 - Engineers move faster because decisions are clear.
 - AI workflows are reliable, debuggable, and measurable.
 - Business teams receive tools that solve real problems, not abstractions.
 - Complexity is removed, not accumulated.

Requirements
Must Have
 - Strong experience designing backend systems (Python preferred).
 - Deep understanding of APIs, async workflows, and modular architectures.
 - Hands-on experience with LLM APIs and AI-assisted system design.
 - Proven ability to reason from first principles and make trade-offs.
 - High ownership: you don’t wait for perfect specs.
 - Excellent written communication in English.

Strong Signals (Not Mandatory)
 - Experience owning 0→1 systems or core platform modules.
 - Background in early-stage or high-ambiguity environments.
 - Demonstrated ability to simplify complex systems.
 - Interest in long-term system thinking rather than short-term feature delivery.

What This Is Not
 - Not a pure coding role
 - Not a people-management role
 - Not a framework-driven “best practices” role
 - Not a title for seniority without ownership

What You Will Gain
 - Real architectural ownership in a founding team
 - Direct impact on how AI systems are designed and used
 - A rare opportunity to shape an internal “Company Brain” from 0→1
 - Long-term growth into system architect or technical founder paths
 - A small, elite, international engineering environment with high trust and low bureaucracy

Location
Shenzhen, China (non-Canada employment).
Remote-friendly during early setup; long-term role is Shenzhen-based.
Close collaboration with global teams across Asia and North America.

How to Apply
Please submit your English resume.
We value clarity of thinking over keyword density.

Privacy notice: Your application will be processed in Canada, the US and China, and retained for 2 years; contact us to withdraw.