Founding ML Engineer

Today, buying something means taking all of the risk.

You spend money, hope you'll still want it later, and if you decide to sell it, you're stuck doing the work yourself.

We think ownership should be liquid.

We're building a future where intelligent systems coordinate demand, supply, pricing, and transactions automatically, giving every purchase a clear path to resale and eventually a guaranteed exit.

TRACE is the first step toward that future.

Today, users can list what they plan to sell before they buy it. Buyers bid, demand forms instantly, and purchases can start paying for themselves before checkout.

Momentum is building quickly.

• We launched 2 months ago
• Early engagement metrics are outperforming marketplace benchmarks by 4-5x
• We closed a $3M seed round last month and are venture-backed
• We're a two-founder team based in NYC
• Our technical CEO spent five years as a Senior Engineer at Etsy and built the first version of TRACE herself
• We recently graduated from Harvard Business School

Now we're building the intelligence layer that powers what's next.

You'll be one of our first engineering hires and the person helping define how machine learning and AI shape the future of TRACE.

You'll work directly with the founders and own systems end-to-end, from data infrastructure to models to production systems.

This is an opportunity to help build a category-defining company from the earliest days.

The core problem is simple to describe and difficult to solve:

How do you match a buyer to an item that hasn't even been purchased yet, and do it accurately enough that people trust the result?

TRACE sits on a unique dataset: demand before ownership.

Every bid, save, search, listing, and transaction creates signals about intent, demand, pricing, and future supply.

Your job is to turn those signals into products.

• Matching systems that connect buyers with future supply
• Demand prediction and marketplace intelligence
• Ranking, recommendation, and search systems
• AI-powered listing creation
• Production ML systems that directly impact user experience
• Feedback loops that improve with every transaction
• Long-term agentic systems that coordinate buyers, sellers, pricing, and transactions

We're looking for someone who enjoys ambiguity, likes owning the full stack from data to production, and gets excited by hard problems that don't have obvious answers.

• You've shipped ML systems into production
• You enjoy owning the entire system, not just the model
• AI tools are part of your daily workflow
• Ambiguous, unsolved problems excite you
• You're interested in recommendation systems, matching, search, marketplaces, or agentic systems
• You want to work closely with product, design, and users

NYC preferred.

If you're exceptional and remote, we'll make it work.

Competitive salary meaningful equity.