Senior AI Product Builder, ShipBob

• 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. Sortation technology experience is a strong plus
• 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
• We recognize that people come with a wealth of experience and talent beyond just the technical requirements of a job. If your experience is close to what you see listed here, please still consider applying. Diversity of experience and skills combined with passion is a key to innovation and excellence; therefore, we encourage people from all backgrounds to apply to our positions

Desirable

• 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 to production 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
• 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
• Additional duties and responsibilities as necessary
• 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)