Senior AI Product Builder, Inventory Experience

ShipBob, Inc.

13d•$151,080 - $251,800•Remote

About The Position

AI Builders is ShipBob’s new job family — the deliberate next step for Product Management in the AI-native era. The thesis is simple: when AI tools can generate working code from precise specs, the slowest step is no longer the spec — it is the PM→Engineer handoff. AI Builders close that gap by owning discovery, specs, prototypes, and production PRs themselves, accelerated by an AI-native pod. As a Senior AI Product Builder at ShipBob, you own the full build loop for your product domain: discovery, story/specs, AI-augmented prototypes, and production-ready PRs. This is not the traditional PM role — you are a builder. You use the AI development toolchain (e.g., Claude Code, Driver.ai) to understand ShipBob’s codebase architecture, generate working code against the actual repo, and ship with minimum engineering rework. The minimum technical floor for this role is real: you must read ShipBob’s data model, evaluate AI-generated scaffolding for correctness, and write story/specs precise enough that AI tools can act on them without follow-up questions. Your domain is Product and Inventory Experience — the system of record for what merchants sell and what they have: the product catalog (SKUs, variants, bundles, kits), inventory records across the network, and the receiving flows that bring stock into it. PIX sits upstream of everything in Merchant Tech — forecasting, placement, allocation, and routing all inherit its data quality. When the catalog is wrong, everything downstream is wrong, which makes PIX one of the highest-leverage product surfaces in the company. This role is for someone energized by speed, uncomfortable with waste, and who measures success by outcomes shipped — not documents approved. You operate in an AI-native pod alongside talented engineers who are ready to support you and push back on solution when needed. This role reports to a Director, AI Product Builder.

Requirements

5+ years of product management, product engineering, or equivalent experience, with demonstrated hands-on AI tool usage in a professional context.
Proven ability to write story/specs that AI tools can act on directly — state machines, business rules, data contracts, edge cases — not narrative PRDs.
Technical fluency: can read a data model, evaluate an API contract, and identify when AI-generated code deviates from the target architecture.
Track record of shipping working prototypes or production code, not just Figma mocks or slide decks.
Strong outcome ownership: sets success metrics and kill thresholds at spec time and tracks them after launch.
Experience working in supply chain, fulfillment, logistics, transportation, or e-commerce technology.
Comfortable operating in a fail-fast culture. Being wrong fast is a feature here — staying slow to look right is the failure mode.
Excellent written communication: you write specs, not meeting notes.

Nice To Haves

Hands-on experience with the AI development toolchain (e.g., Claude Code, Driver.ai, Cursor, Replit, V0, Figma AI).
Working knowledge of TypeScript, React, or Python — enough to debug AI-generated code and understand what the Engineer is reviewing.
Experience with product information management, catalog or inventory systems, multichannel commerce integrations (e.g., Shopify, Amazon), or WMS receiving flows at scale.
Prior experience transitioning a product team from traditional SDLC to an AI-native operating model.

Responsibilities

Own the full product loop end-to-end: AI-augmented discovery → story/spec → prototype → production PR → outcome measurement. You are accountable for the outcome, not just the handoff.
Own the product catalog and inventory data surface end-to-end: SKUs, variants, bundles, lots, and channel sync. Define the data contracts merchants and partners build against, ship the workflows, and measure data quality and adoption.
Run customer discovery yourself; use AI tools to synthesize research across tickets, interviews, behavioral data, and competitive signals.
Demonstrate deep functional and technical knowledge of owned products end-to-end. Demo to merchants and partners yourself; understand competitive landscape and key differentiators.
Use AI tools to analyze product metrics, identify usage patterns, and surface anomalies. Define success metrics and kill thresholds at spec time — without coaching.
Maintain a prioritized roadmap ranked by impact × speed. Use AI tools to synthesize competitive intelligence, market trends, and customer insight into strategic inputs.
Evaluate whether a proposed solution fits ShipBob’s architecture without Engineering translation. Understand service boundaries, data structures, and how merchant and partner API usage shapes data contracts.
Route changes through prototype-then-PR. Write clear story/specs and review AI-generated code before raising. PR promotion rate improves consistently.
Set experiment hypotheses with explicit kill thresholds. Make day-7 rollout/kill decisions on AI-surfaced data.
Participate in architecture reviews alongside Engineering as the product judgment seat — the person making the build-vs-buy-vs-defer call on the room’s behalf.
Additional duties and responsibilities as necessary.