Gigi is the agentic operating system of enterprise media buying. Our first product is the AI media manager for Amazon DSP. Since launching it in summer 2025, we've grown to manage hundreds of millions in advertising spend on behalf of the largest agencies in the world.
We're backed by top venture investors Golden Ventures and Aperiam Ventures. Our founding team has spent 15+ years building in Amazon Ads and ecommerce, including a prior $100M+ exit.
We doubled revenue in the past two months. We expect to double again by early Q4.
We're hiring an AI Builder - a full stack software engineer - to join our engineering team in Toronto and own end-to-end feature development for Gigi, our AI agent.
This is still fundamentally a software engineering role. Much of the work will feel familiar: designing APIs, building interfaces, modelling data, integrating services, writing tests, debugging production issues, and making architectural decisions. The main difference is the product we're building: agentic software where LLMs reason, use tools, and take actions on behalf of customers.
You do not need to be an AI researcher or have years of professional LLM experience. Strong full stack engineers who are excited to learn how agentic systems work should apply. You'll use tools such as Codex, Claude Code, and Cursor as a core part of how you work, while building across agent logic, backend services, data, and the customer-facing product.
This is the 12th role at Gigi. As an early employee, you will have a meaningful role in shaping our product, engineering culture, and company.
What You'll Do
- Ship end to end. You'll own features from the data layer through the agent logic to the interface a media buyer actually touches. Our stack includes Python, FastAPI, LangGraph and LangChain; TypeScript, Vue and Nuxt; Java and Spring Boot; PostgreSQL, pgvector, Redis and NATS; and AWS.
- Build with AI as a core part of your development workflow. Use Codex, Claude Code, Cursor, and other coding agents to help plan, implement, test, debug, and review software. You are still responsible for the architecture, quality, and correctness of what gets shipped.
- Build the agent's judgment, not just its plumbing. Decide what Gigi should do autonomously and what it must escalate to a human, then score actions on risk and confidence so recommendations read differently when the data is thin.
- Draw the line between probabilistic and deterministic work. LLMs for reasoning, structured software for execution. Knowing where that boundary sits on any given feature is a daily call, not a settled architecture.
- Build reliable agentic systems. You'll learn to design tools, retrieval, memory, evaluations, guardrails, and human approval flows that make Gigi dependable enough to take action inside real advertising accounts. Prior experience with all of these is not expected.
- Work directly with customers. You'll sit in on calls, read transcripts, and take feedback straight from the media buyers using the product. You'll turn ambiguous customer problems into shipped product without waiting for a perfect spec.
What Success Looks Like
- First 30 days. Your code is in production. You understand what a media buyer's day actually involves and why the product is shaped the way it is.
- Six months. You own a surface area of the product outright. When something breaks in it, or a customer asks for something new, you're the person who decides what happens.
- Twelve months. You've shipped something we could not have built without you, and you've thrown out at least one thing you built because the abstraction was wrong.
What You'll Bring
- A track record of building and shipping production software used by real customers. We care more about what you have built and owned than an exact number of years.
- Strong full stack software engineering experience. The fundamentals you already have - system design, APIs, data models, interfaces, testing, debugging, and operating production software - transfer directly to this role.
- Comfort across the stack. Real depth somewhere, no allergy to the parts you're weaker in.
- Experience using, or a strong desire to become highly effective with, AI coding tools such as Codex, Claude Code, Cursor, or similar tools. You do not need to be an expert on day one, but you should be excited to use them every day - and strong enough technically to review their work, reject weak abstractions, catch missing edge cases, and remain accountable for what ships.
- Curiosity about how LLMs change software products and the way software gets built. You should be excited to develop good judgment around where AI belongs - and where conventional software is the better answer.
- Judgment over specification. You can make a reasoned call, ship it, and iterate fast when it's wrong.
- Speed and ownership. LLMs changed what “fast” means for a small team. You should be comfortable using AI coding agents to move quickly while maintaining a high bar for architecture, correctness, and maintainability.
What You Don't Need
- You do not need an AI research background or experience training foundation models. We build products around existing models, combining them with tools, data, business logic, interfaces, and production infrastructure.
- You do not need prior experience with every agent framework or concept we use. We expect strong engineers to learn LangGraph, agent orchestration, evaluations, retrieval, and tool use on the job.
- You do not need prior advertising or Amazon DSP experience. Our team will help you learn the domain.
Bonus Points If
- You've built with LLMs in production: evals, guardrails, tool use, retrieval, agent orchestration, cost and latency work.
- You've worked in advertising or ecommerce.
- You've built real-time data pipelines at meaningful volume, or been early enough at a startup to know what the first 20 people actually do.
- You've operated production systems where correctness, permissions, auditability, or customer trust mattered.
The Hard Parts
We'd rather you know this before you apply.
- We work hard. We are trying to build a generational company, and early employees should expect intensity, ambiguity, and high standards.
- Cash compensation is competitive for our stage, but we will not win every late-stage salary comparison. The upside is meaningful ownership and equity in a company growing quickly.
- We throw out working code when the underlying abstraction is wrong. Some of what you build will not survive, and you'll be the one who calls it.
- Nobody is siloed. You'll write frontend, debug backend, think through product, join customer calls, and solve whatever problem is blocking the customer.
Compensation and Equity
- Base salary: $150k to $200k depending on experience and talent level
- Meaningful equity. We want to be as generous with equity as possible, and top performers can expect equity top-ups early and often.
Life at Gigi
- In-office culture. We work together downtown Toronto, but makers (i.e. engineers) can choose to work where they are most productive on Wednesdays. Our office is right across from Union Station
- Catered lunch every day from Toronto's best spots. Current rotation: Impact Kitchen, iQ Food Co., ChopHop, and Cumbrae's.
- Real snacks: Greek yogurt, Mateína yerba mate, Barebells protein bars, Midday Squares. Tell us what you want and we'll add it.
- Top-of-the-line Apple equipment.
- Comprehensive health and dental coverage.
