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Traba is building the AI operating layer for the industrial supply chain, starting with contingent labor in manufacturing and logistics. The company has embedded itself deeply in customer operations and now is expanding into broader operational workflows. Backed by Founders Fund, Khosla Ventures, and General Catalyst, Traba is launching an agentic platform that synthesizes marketplace data and operates autonomously within supply chain workflows.
As Staff Product Manager for the AI Agents team, you will be a founding member architecting the next layer of Traba's product—an applied AI system for light industrial businesses. This is a true 0-to-1 role at the frontier of applied AI, where you'll design and ship production agents that understand context, reason through ambiguity, make decisions, and execute work on behalf of users.
Key responsibilities include: architecting and stewarding product strategy for the AI agent platform; solving complex customer problems by balancing user needs, technical constraints, and evolving AI capabilities; writing detailed product requirements and partnering with product engineers and ML teams; rapidly validating and prototyping ideas with customers and design partners; designing agent systems that reason through workflows, orchestrate tools, maintain context, and execute meaningful work; defining product behavior, evaluation systems, and quality frameworks; using data to uncover patterns and answer key product questions; evolving the AI product portfolio with deep understanding of model capabilities; and communicating complex concepts across all organizational levels.
You will report directly to the VP of Product and work alongside founders, engineers, ML teams, and early design partners. The role demands someone who has launched customer-facing AI products where systems took meaningful autonomous actions—not just AI features, copilots, or chat interfaces. You should be a 0-to-1 operator comfortable building products from scratch with real customers, an AI-native problem solver who moves quickly as models evolve, a first-principles thinker who understands systems deeply, and a customer-embedded builder who develops products shoulder-to-shoulder with early adopters. Technical depth is essential; you should be comfortable operating as a peer alongside ML engineers, understanding evaluations, reasoning through system tradeoffs, and participating in technical conversations.
