Full Stack Engineering Intern

Full Stack Engineering Intern

San Francisco

Logistics is the substrate beneath every economy that has ever existed, and it remains the least intelligently coordinated activity in the modern world. Consumer expectations are converging on instantaneous, perfect, free. Networks are not. We call this gap the Logistics Singularity, and closing it is the work.

Nash is the Autonomic Logistics OS. We unify decisioning, execution, and capacity into a single programmable system that pursues a business's objectives continuously, adapts as conditions change, and runs the operation at equilibrium across orders, fleets, carriers, providers, and customers. The world's largest retailers, grocers, and pharmacies (including Walmart, 7-Eleven, Woolworths, and Coles) run their critical workflows on Nash.

Founded by Mahmoud Ghulman and Aziz Alghunaim, and backed by Y Combinator, a16z, and other top investors. We are based in San Francisco.

We're looking for a Full Stack Engineering Intern to work on Nash's intelligent and agentic layer: the part of the system that senses conditions on the ground, decides what should happen next, and acts. Agents that triage exceptions, recover routes mid-shift, answer operator questions, and intervene before a promise breaks. You'll work across React and Python to build the interfaces operators trust and the agents working alongside them.

This isn't a sandbox internship. You'll pick up real work on day one, ship code customers use, and leave with something concrete to point to. The right person here is as interested in how an agent reasons as in how its decisions show up in the interface. Agents are only as useful as the trust operators place in them, and that trust is built or broken in the product.

• Ship features end to end across Nash's agentic layer, from the React and Type Script surfaces where operators work with agents, to the Python services and LLM workflows that power them.

• Build agent capabilities that resolve real operational problems: rerouting around a closure, reassigning a stuck order, surfacing the right answer to a dispatcher mid-shift.

• Translate logistics problems into product. Sit in with PM, design, and operations to turn messy real-world workflows into systems that fit how customers actually run their business.

• Contribute to system design discussions on how agents reason, when they act on their own, and when they hand off to a human, with senior engineers around the table to push your thinking.

• Write clean, well-tested code, participate in code reviews, and pick up the standards a strong engineering team runs on.

• Debug across frontend, backend, and model behavior, with the observability and evals that catch problems before customers do.

• Build responsive interfaces with a sharp eye for latency, loading and error states, and the edge cases that decide whether an operator trusts an agent's recommendation.

• Currently pursuing a degree in Computer Science or a related field, or recently graduated.

• Hands-on experience building full-stack web applications, through coursework, side projects, prior internships, or open source.

• Working knowledge of Python, React (or an equivalent modern framework), Type Script, HTML, and CSS.

• Familiarity with REST APIs and relational databases (PostgreSQL preferred). Exposure to GraphQL or No
  
  SQL is a plus.

• Curiosity about LLMs, agents, and AI systems. Prior experience building with them is a plus, not a requirement.

• Product sense and craft. You care about how things feel, not just whether they work.

• A strong communicator who can collaborate across disciplines and drive ambiguous problems toward a clear outcome.

• High agency. You don't wait to be told what to do next.

San Francisco office ONLY.

The intelligent and agentic layer is the part of Nash that turns raw signal into action. It's where a thousand small decisions get made every minute: which driver, which route, which fallback, which intervention. Done well, it's the difference between a promise kept and a customer call. The agents and interfaces you contribute to will run inside operations at some of the largest retailers,…