Senior AI Product Builder - Sortation Technology

ShipBob, Inc.

2d•$151,080 - $251,800•Remote

About The Position

As a Senior AI Product Builder at ShipBob, you will own the full build loop for your product domain, encompassing discovery, story/specs creation, AI-augmented prototyping, and production-ready pull requests (PRs). This role deviates from the traditional Product Manager position, as you are expected to be a builder. You will leverage the AI development toolchain (e.g., Claude Code, Driver.ai) to understand ShipBob's codebase architecture, generate functional code directly within the actual repository, and deploy to production with minimal engineering rework. A strong technical foundation is essential; you must be capable of reading ShipBob's data model, assessing the accuracy of AI-generated scaffolding, and writing precise story/specs that AI tools can execute without further clarification. You will be an integral part of the Sortation Technology team, responsible for the products governing every stage of inventory sorting within ShipBob's operations. This includes inbound presort during receiving, outbound parcel sortation for final-mile injection, B2B freight sortation for building retail or wholesale pallets, and returns sortation for routing units back to inventory or disposition. As e-commerce volume grows across time-sensitive marketplace and B2B channels, sortation directly impacts merchant outcomes such as on-time shipping, marketplace and retailer compliance, inventory accuracy, and the end-consumer tracking experience. Sortation operates at the critical intersection of physical operations and digital systems, where minor execution errors can lead to significant merchant-visible failures. This Product Manager role is therefore highly leveraged, owning the products that ensure units are directed to the correct location at the appropriate time, and that system information accurately reflects floor operations. This position is ideal for an individual driven by speed, intolerant of waste, and who prioritizes shipped outcomes over approved documentation. You will function within an AI-native pod alongside an AI Engineer, AI/Prompt Engineer, and Designer, reporting 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 warehouse management systems, order management, carrier integration, or fulfillment operations at scale.
Prior experience transitioning a product team from traditional SDLC to an AI-native operating model.
Sortation technology experience is a strong plus.

Responsibilities

Own the full product loop independently: AI-augmented discovery → story/spec → prototype → production PR → outcome measurement. You are accountable for the outcome, not just the handoff.
Conduct customer discovery and 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; 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.
Translate merchant, marketplace, and channel requirements into sortation capabilities across the fulfillment lifecycle. Sortation performance shapes outcomes for DTC, marketplace, B2B/retail, and returns flows: on-time shipping, marketplace and retailer routing-guide compliance, inventory accuracy, and accurate returns disposition. Sequence the roadmap around the highest-impact pain points.
Close the gap between system signals and physical reality across every sortation surface. Sortation failures show up as merchant-visible defects: late shipments, mis-sorts, missing tracking events, mis-routed returns, and inventory inaccuracy at stowing. Build the tooling, workflows, and verification signals that prevent them.
Partner with Operations to turn product into adoption. Work directly with FC, Sort Center, Inbound, and Returns leadership, alongside Process Engineering and Transportation. Translate new tools into standard operating procedures and measurable performance improvement across every sortation surface — from manual sort to future automated unit sorters.
Move the metrics that define sortation performance — outbound execution SLAs (Click-to-Collect, Mis-sort Rate, Container Audit Defect Rate), inbound rebuild outcomes (drop-to-stock, SLA adherence), B2B compliance (SLA, load accuracy, retailer routing-guide compliance), and returns processing (cycle time, disposition accuracy).
Additional duties and responsibilities as necessary.