• Define and own the internal AI tooling roadmap, prioritizing by business impact and feasibility. • Partner with department leads (Sales, Support, Engineering, Finance, etc.) to surface automation opportunities. • Evaluate and select AI models, platforms, and SaaS tools that fit our stack and budget. • Prototype, build, and ship AI-powered internal tools — chatbots, workflow automations, document processors, and data pipelines. • Integrate LLMs and AI APIs (OpenAI, Anthropic, etc.) into existing systems via APIs, Zapier, Make, or custom code. • Maintain a library of reusable prompt templates, fine-tuned workflows, and automation playbooks. • Provide regular and on demand training across the organization. • Establish guardrails, usage policies, and monitoring for AI tools used internally in cooperation with our Legal team and CISO. • Track cost, quality, and adoption metrics for every tool deployed; iterate based on data. • Ensure all tooling meets security, privacy, and compliance standards.

• 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. • Independently 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. Independently demo to merchants and partners; understand competitive landscape and key differentiators. • Use AI tools independently to analyze product metrics, identify usage patterns, and surface anomalies. Define success metrics and kill thresholds at spec time without coaching. • Maintain a prioritized automation roadmap ranked by impact × speed. Use AI tools to synthesize competitive intelligence, market trends, emerging warehouse automation capabilities, and customer insight into strategic inputs. • Identify the highest-leverage productivity and quality opportunities in ShipBob’s fulfillment operations. Evaluate automation hardware options, recommend the right strategic fit, and own the WMS-side product work that integrates them into the floor. • Independently 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. • Independently 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.

• 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 ShipBob's MCP server and merchant-facing chat capabilities end-to-end. Define the agent surface, ship the integrations, measure 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. • Your first 90 days: • Days 1–30 — Map the domain. Understand the ShipBob operating and commercial mode, examine the ODM codebase, anchor on the purpose and value of the domain, and stakeholder conversations to triangulate the top three active bets. Demo a throwaway prototype back to the pod. • Days 30–60 — Ship your first PR. Small and safe is fine. The point is proving the loop, not the size of the change. • Days 60–90 — Make a real call. Define and instrument the metric for one real bet, with the kill threshold set at spec time. Make at least one rollout-or-kill call on the data, in front of the team.

• 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 evaluate 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.