Backend Engineer

About the role

What we're building

Channel3 is building a database of every product on the internet — the product catalog and search infra that lets agents shop. We see Channel3 becoming as foundational to agentic commerce as Stripe is to payments or Plaid is to fintech.

What we believe

Agentic commerce is inevitable. The opportunity is massive. The biggest problem — product discovery — is unsolved, and we’re the best-positioned company to solve it.

The team

Alex (CEO) and Jade own GTM.

George (CTO), Evan, Ignacio, and Jungyun own engineering. We’re looking for two more engineers to join us!

Our progress so far

- We’ve indexed 100M+ products

- 3,000+ developers have started using our API

- Startups and public companies use our API to power their shopping platforms

- We’ve partnered with 5,000+ brands, including Walmart, Best Buy, Shopbop, Staples, and Home Depot, who share product feeds and pay commission on sales

What you’ll do

- Write code to understand 1B products. We stitch together the latest language, embedding, image, and segmentation models to build a true understanding of each and every product.

- Product pages vary greatly across retailers. We’ve built a system that can understand any PDP, with no custom code or manual intervention on any site.

- Deduplicating products across merchants is really hard. Products are described differently, often have different images, and almost never have stable identifiers. We use multimodal models to help us understand different products and match them.

- Understanding what variants a product comes in might be even harder. We want to know what colors and sizes every shirt comes in, and what configurations customers can order for a new couch.

- Build world-class search. Developers should be able to find “outdoor grills from Weber, less than $1000, with 4 burners,” “running sneakers under 300g, size 12,” or “this couch [image] but in green” — in under a second.

- Embeddings don’t get us there. We’re always looking to add structured data that lets developers construct powerful queries with deterministic filters.

- Lately, we’ve been ideating about how to return “good” results, which we’ve learned is not just the highest keyword/semantic match.

- Engineer for reliability and cost. Create evals to measure drift, and guardrails to prevent regressions and hallucinations. Be clever and scrappy to reduce database cost and token usage everywhere we can.

- We use billions and billions of tokens a month, mostly on open-source models. We embed every product, we look at every image with AI, and we run dozens of calls per product — it’s necessary, but it adds up. We’re always looking to reduce token usage, rely on a smaller model, or do some clever context engineering to reduce input tokens.

- We structure the world’s product data — and we have to store it and search through it (both internally to find matches and to power our API). We’ve taken many steps towards making this economical, and will continue to do so.