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're hiring a Forward Deployed Engineer - Ads to join our engineering team in Toronto and own end-to-end execution of Gigi's agent-to-agent collaborations with our agency customers' proprietary tech and with third-party agents in analytics, measurement and creative.
This is a customer and partner facing software engineering role. You'll work directly with agency and partner teams to scope each integration, then design, build, launch and support it in production. Much of the work will feel familiar: designing APIs, modelling data, integrating services, writing tests, debugging production issues, and making architectural decisions.
As an early employee, you will have a meaningful role in shaping our product, engineering culture, and company.
What you'll doDiscovery. Lead working sessions with agency leaders, media teams, and partner product and engineering teams. Understand their workflows and systems, explain tradeoffs in plain language, and turn needs into a clear scope.
Agent-to-agent integrations. Design and build how Gigi connects to agency platforms and third-party agents using MCP, A2A and APIs: what context comes in, what actions Gigi takes, and what results flow back.
Core engineering. Design APIs and data models, build backend services and data pipelines, ship frontend where the integration needs it, write tests, and review code. Our stack includes Python, FastAPI, LangGraph and LangChain; TypeScript, Vue and Nuxt; Java and Spring Boot; PostgreSQL, pgvector, Redis and NATS; AWS; Amazon Ads API and AMC.
Reliability. Handle authentication, permissions, client-level data separation, human approval steps, audit logs, rate limits, retries and monitoring so Gigi can act safely inside live ad accounts. Debug and fix production issues when something breaks.
Launch and support. Roll integrations out with customers, help their teams adopt them, and own them in production. Communicate progress and setbacks clearly.
Product. Work with Product and Engineering to decide what becomes a reusable connector, a shared feature, or a customer-specific configuration, so each new integration is faster than the last.
Build with AI. Use Codex, Claude Code, Cursor and other coding agents every day, while staying accountable for architecture, quality and correctness.
In person. Travel to agency and partner offices for discovery, working sessions and launches.
First 30 days. Your code is in production and you've shipped an improvement to a live partner integration.
Six months. You own several agency and partner integrations end to end, and their teams treat you as their technical counterpart.
Twelve months. You've built a reusable integration layer that makes new agency and partner integrations fast to launch.
Advertising industry experience at an ad tech company. You understand how campaigns are planned, bought, measured and reported.
Customer or partner facing experience, such as solutions engineering, forward deployed engineering, technical account management or partner engineering. You can lead a discovery call and present to agency leadership.
A track record of building and shipping production software used by real customers, ideally integrations, APIs or data pipelines across systems you didn't own.
Strong full stack software engineering experience. System design, APIs, data models, testing, debugging, and operating production software are central to this role.
Experience using, or a strong desire to become highly effective with, AI coding tools such as Codex, Claude Code, Cursor, or similar tools, and strong enough technically to review their work and remain accountable for what ships.
Speed and ownership. You make a reasoned call, ship it, and iterate fast when it's wrong.
You do not need an AI research background. We build products around existing models.
You do not need prior experience with MCP, A2A or every agent framework we use. We expect strong engineers to learn them on the job.
You've worked with Amazon DSP, DV360, The Trade Desk, AMC or clean rooms.
You've built integrations between AI agents or LLM-based systems in production.
You've operated production systems where correctness, permissions, auditability, or customer trust mattered.
You've been early enough at a startup to know what the first 20 people actually do.
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.
Base salary: $150k to $210k depending on experience level.
Meaningful equity. We want to be as generous with equity as possible, and top performers can expect equity top-ups early and often.
In-office culture. We work together downtown Toronto (min 4x days per week), but makers (i.e. engineers) can choose to work where they are most productive on Wednesdays. Note: we're moving into a new office right across from Union.
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.
