Senior Product Manager, AI

Instacart

18h•CA$194,000 - CA$204,500•Remote

About The Position

Instacart’s AI team is building B2B agentic AI products that help retailers and CPGs recover margin by tackling out-of-stock, shrink and waste, and fulfillment utilization. We aim to translate cutting-edge AI into practical, trustworthy solutions that move key financial levers for our customers. As a Product Manager, AI, you will own the vision, roadmap, and go-to-market for new agentic AI capabilities from 0 to 1. You’ll work backwards from customer outcomes — not technology trends — to define clear product requirements, partner closely with engineering and data science to ship value quickly, and collaborate with sales and marketing to craft a commercial motion that proves impact and scales revenue. You’ll join a small, focused team (alongside 2 PMs and 7 engineers to start) with the mandate and resources to move fast. If you thrive in ambiguity, love turning complex data and workflows into simple, outcome-driven products, and want to shape how AI transforms retail operations, this role is for you.

Requirements

6+ years of product management experience building B2B or enterprise software, including 3+ years delivering AI/ML-powered products from concept through GA.
Proven record shipping at least 2 AI/ML products used by enterprise customers with measurable financial outcomes (e.g., margin improvement, cost reduction, or productivity gains).
Hands-on data proficiency, including the ability to query or analyze large datasets (e.g., SQL or equivalent) and evaluate data quality, model performance, and signal reliability.
Experience leading cross-functional execution with engineering, data science, design, sales, and marketing to launch and scale products.
Demonstrated ability to build ROI and unit economics models and present business cases to executive audiences.
Experience navigating complex enterprise sales cycles, including security, IT, and legal/procurement processes.
Exceptional written and verbal communication skills; able to translate technical concepts for commercial audiences and commercial concepts for technical audiences.
Bachelor’s degree in a technical, quantitative, or business field (e.g., Computer Science, Engineering, Mathematics, Economics) or equivalent practical experience.

Nice To Haves

Experience in grocery, retail, or supply chain with familiarity in store operations, inventory management, and key margin levers.
Exposure to agentic AI systems, LLM-based workflows, autonomous decision pipelines, prompt engineering, and AI evaluation/guardrail practices.
Background with demand forecasting, replenishment, or inventory optimization products and metrics (e.g., OSA, service level, shrink).
Familiarity with the retail tech stack, including WMS, POS, ERP/replenishment modules, EDI, and API-based integrations.
Experience displacing incumbents or winning competitive takeouts in enterprise accounts.
Early-career experience in management consulting, solutions architecture, or business analytics; comfortable running diagnostics and leading with financial outcomes.

Responsibilities

Own the product vision, strategy, and roadmap for agentic AI solutions that reduce out-of-stocks, shrink and waste, and improve fulfillment utilization for retailers and CPGs.
Work backwards from customer outcomes to write crisp problem statements, PRDs, and success metrics; prioritize ruthlessly to deliver v0, iterate to v1, and scale proven features.
Partner with engineering and data science to scope experiments, manage backlogs, define KPIs and evaluation frameworks, and ensure models and agents are reliable, safe, and observable.
Lead discovery with customers; design pilots and proof points; collaborate with sales and marketing on go-to-market, pricing and packaging, enablement, and case studies that drive revenue.
Own the business case: model unit economics and ROI, set financial targets for margin recovery/efficiency, and report performance to executives and external stakeholders.
Ensure data readiness and integrity by assessing data quality, telemetry, integrations, and feedback loops; implement human-in-the-loop and policy guardrails where appropriate.
Operate cross-functionally with data engineering, retailer success, enterprise sales, and BD to bring products from concept to launch and expansion.