New Grad AI Engineer

San Francisco / New Yorkfull-timemid

Machine Learning EngineerData

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Quick Summary

Key Responsibilities

Build the AI systems that automate real trade workflows: collections, credit decisioning, payments operations, and the manual work that slows the physical economy down.

Technical Tools

Machine Learning EngineerData

We’re Redefining the $11T B2B Commerce and Payments Market

Nuvo is bringing B2B commerce online.

While technology has reshaped consumer commerce, the business-to-business (B2B) economy — three times larger— has lagged behind.

From coffee to lumber, every product we use has been traded through a complex supply chain of suppliers, manufacturers, wholesalers, and retailers. Despite the massive scale of this market, most businesses manage their trading relationships and commerce activities using outdated methods like paper forms, PDFs, emails, faxes, phone calls, and spreadsheets, making processes slow and inefficient.

Much like how Shopify, Stripe and Square have transformed how consumer companies transact, we aim to do the same for B2B companies.

About the Role

~2 min read

We are looking for smart engineers to help build category-defining products. Our problems span scaling AI agents, multi-agent workflows that automate risk and payments, and running agents on large scale data. You'll partner with our engineering, design and product teams to help shape the roadmap, solve real world problems and build our 0-1 products. Your work will have huge impact: on team culture, on our customers and on the economy.

What You’ll Do:

Build the AI systems that automate real trade workflows: collections, credit decisioning, payments operations, and the manual work that slows the physical economy down.
Turn LLMs and agents into reliable, production-grade features, not demos, with the evals and guardrails to back them.
Write high-quality, clean and performant code and partner with platform and product engineers to ship end to end.
Help shape how Nuvo builds with AI as both the field and our product move quickly.
Use the best tools for the job. What matters is whether the system works in production, not which model or framework you used.

What You Bring:

Strong CS fundamentals plus hands-on experience building with modern ML or LLMs (coursework, research, internships, or projects you can show).
Good judgment about where AI genuinely helps versus where it adds risk, and a rigorous approach to evaluating output quality.
The ability to communicate technical solutions and tradeoffs clearly.
Excitement to work in an early-stage team solving ambiguous problems with a strong bias for action.