Senior Product Manager; AI Agents

About Traba

Traba is building the AI operating layer for the industrial supply chain.

We started in the workforce space because contingent labor is the biggest operational pain point our manufacturing and logistics customers face. We used it to embed ourselves inside their daily operations and create a far better customer experience through technology. Now those same customers are pulling us beyond staffing into the broader operational workflows that run their facilities. That foundation gave us proprietary data from millions of shifts and deep enterprise relationships.

But our edge is more than data. By connecting to the systems running across every facility and activating the workers already on our platform to execute against them, we are building applied AI that drives real productivity gains and transforms how the global supply chain operates at scale.

We are backed by Founders Fund, Khosla Ventures, and General Catalyst.

We are looking for an entrepreneurial and experienced Product Manager to join as a founding member of the Agents team and help us build the next layer of Traba's product, an agentic platform that synthesizes the data flowing through our marketplace and operates autonomously inside our customers' supply chain workflows. Think Claude, but for Light Industrial businesses.

You will embed deeply in our customers' operations, design and ship production agents that understand context, reason through ambiguity, make decisions, and execute work on behalf of users. This is a true 0-to-1 product role operating at the frontier of applied AI.

You will work directly with founders, product engineers, ML teams, and early design partners to define workflows, shape product strategy, and build products while the underlying model ecosystem continues changing beneath us. The assumptions, capabilities, and constraints will evolve rapidly and the product will evolve with them.

This role reports directly to our VP of Product.

• Architect and steward product strategy for Traba’s AI agent platform

• Solve complex customer problems by balancing user needs, technical constraints, and evolving AI capabilities

• Write detailed product requirements and partner deeply with product engineers and ML teams

• Rapidly validate and prototype ideas alongside customers and design partners

• Design agent systems that reason through workflows, orchestrate tools, maintain context, and execute meaningful work

• Define product behavior, evaluation systems, and quality frameworks that improve reliability and performance

• Use data to uncover patterns and answer key product and system questions

• Evolve our AI product portfolio with a deep understanding of model capabilities and system architecture

• Visualize and communicate complex concepts from system behavior and evaluations to user experiences and business impact

• Socialize your work across all levels and departments while synthesizing diverse stakeholder feedback

Real agent builder. You have launched customer-facing AI products where the system took meaningful actions on behalf of users. We are intentionally setting a high bar here. Shipping AI features, copilots, chat interfaces, wrappers, or lightweight LLM integrations alone is not enough. You understand agents that execute workflows, orchestrate systems, use tools, maintain context, and perform work autonomously.

0-to-1 operator. You have built products from scratch with real customers and understand the realities of finding product market fit. You have launched, rebuilt, changed direction, killed ideas, and iterated toward signals. You are comfortable creating structures where no playbook exists.

AI-native problem solver. You move quickly in environments where models evolve constantly and assumptions regularly break. You understand that building AI products often means navigating prompt rewrites, model shifts, reliability tradeoffs, and capability cliffs.

First-principles thinker. You spend equal time thinking about why, how, and what. You understand that strong product judgment often comes from deeply understanding systems and constraints rather than applying generic playbooks.

Customer-embedded…