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Staff Product Manager (AI Agents)

Traba · San Francisco, United States

External listingfull-time6 days ago

About The Role

Join Traba, an early-stage startup revolutionizing the light industrial sector. As a Staff Product Manager, you'll lead the development of an AI agent platform that integrates seamlessly into our customers' supply chain workflows. This is a unique opportunity to work at the forefront of applied AI, collaborating closely with founders, product engineers, ML teams, and design partners. You'll have a direct impact on shaping product strategy and driving meaningful customer outcomes.

  • Architect and steward product strategy for Traba’s AI agent platform, solving 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 to 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, while defining product behavior, evaluation systems, and quality frameworks.
  • Evidence of launching products from scratch and driving meaningful customer outcomes
  • Experience building AI-native products or customer-facing agent systems
  • 7 – 10 years of experience as a Product Manager
  • A strong work ethic and desire to build a category-defining company
  • Desire to work at a startup and cover an exceptionally wide surface area
  • Experience operating in highly ambiguous startup environments with fast iteration cycles
  • Deep familiarity with AI tools and hands-on experience building with modern AI systems
  • Strong technical abilities with the ability to work deeply with engineering and ML teams
  • Ability to read evaluations, reason through system performance, and prototype ideas
  • 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
  • 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
  • Technical depth. You are comfortable operating alongside ML engineers and highly technical teams as peers. You can understand evaluations, reason through system tradeoffs, prototype ideas, and participate deeply in technical conversations. You do not need to be an ex-engineer, but you should have strong technical instincts and enjoy getting into the details
  • Customer-embedded builder. You believe the best products are built shoulder-to-shoulder with customers. You have experience deeply embedding with early customers, understanding workflows firsthand, and rapidly shipping based on direct feedback loops. Experience with forward deployed environments or customer-embedded product development models is highly valued
  • 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
  • Deeply collaborative. You work seamlessly across engineering, ML, operations, design, leadership, and customers to bring new products to life
  • Builder mentality. Inventive and scrappy problem solver. You move quickly, embrace ambiguity, and know how to create meaningful outcomes without relying on giant teams or excessive process

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