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What success looks like

• First 30 days. You've shipped your first improvements to production, stood up trusted evals on our core workflows, and contributed meaningfully to our roadmap.
• Six months. Every change ships through evals. Context and memory measurably improve recommendation quality per customer. Routing has cut model cost per action without hurting quality.
• Twelve months. A post-trained open-weight model is running in production. The commerce P&L runs live for brands on outcome-based contracts.

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 software engineering experience, with depth in backend or full stack development. System design, APIs, data models, testing, debugging, and operating production software are central to this role.
• Hands-on experience building with LLMs or agents, whether at work, through open source, or in a substantial personal project. You can explain how it works, where it fails, and how you would improve it.
• 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 built evaluation datasets or used production traces and customer feedback to improve an agent's behavior.
• 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 $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.

Life at Gigi

• 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.