AI Engagement Manager

Instacart

8d•$178,000 - $226,000•Remote

About The Position

The Enterprise AI Pod is a forward-deployed, field-first unit within Instacart's Enterprise Solutions team. This team is purpose-built to take the Instacart Intelligence Platform to market with B2B retail and CPG partners. The AI Engagement Manager serves as the operational backbone of these engagements, ensuring complex, multi-stakeholder AI initiatives move forward. This is an orchestration role focused on driving partners to value, not a commercial role. The position owns the partner relationship from discovery through workflow adoption, holds the team accountable for delivery, and acts as the primary bridge between field execution and R&D. The role requires managing multiple engagements simultaneously in an environment where the operational playbook is still being developed.

Requirements

8+ years in technical project management, enterprise engagement management, or a related field.
Track record of delivering complex, multi-stakeholder technical programs end-to-end to external enterprise clientele.
Demonstrated experience managing programs across the full software development lifecycle (requirements definition, scoping, build, test, launch).
Ability to identify and manage risk, develop detailed work plans, and allocate resources across concurrent workstreams.
Technical fluency sufficient to hold credible conversations with partner engineering teams and understand AI/ML implementations and integration dependencies.
Experience building and maintaining engagement cost models, tracking resource utilization, time allocation, and cost inputs.
Ability to translate cost data into cost/benefit analysis that informs business and deal-level decisions.
Exceptional communication, organizational, and time management skills.
Sharp attention to detail and overarching project objectives.
Expertise in facilitating highly productive internal and external meetings.
Ability to create assets and processes that drive project clarity.
Comfort operating in ambiguous, fast-moving environments where structure doesn't yet exist.
Proficiency across program management tools (Jira, Asana, Smartsheet), collaboration and documentation platforms (Confluence, Google Workspace), and executive-level presentation tools (PowerPoint, Google Slides).
Bachelor's degree in Technology, Engineering, Business, or related field, or equivalent practical experience.
Willingness to travel ~50% within North America.

Nice To Haves

Experience managing AI, data platform, or systems integration deployments at enterprise scale.
Background in retail tech, e-commerce, operations, or CPG.
Experience working at the intersection of engineering, product, and commercial teams.
Familiarity with agentic AI platforms, LLM APIs, or RAG-based systems.
Familiarity with CRM tools (Salesforce), BI and reporting platforms (Tableau, Looker, Power BI), and workshop facilitation tools (Miro, Lucidspark).
PMP or equivalent program management credentials.
Prior work in a forward-deployed, embedded, or startup environment.

Responsibilities

Own the full AI engagement arc, from first discovery through sustained workflow adoption, serving as the external partner's single point of contact.
Drive scope, timelines, milestones, and escalations, keeping cross-functional workstreams aligned and moving.
Shape engagement structure, align on objectives, and translate partner needs into clear execution plans, tracking progress with rigor.
Surface risks early and proactively inform stakeholders (partner and internal) of potential problems.
Oversee testing and feedback loops to ensure the platform performs against real partner workflows and track adoption progress.
Manage the change management work necessary for deployment to become lasting behavior change.
Ensure field learnings flow back to R&D and that R&D delivers on agreed roadmap commitments, owning escalations when the loop breaks down.
Identify reusable patterns across engagements and develop them into scalable delivery frameworks for the pod.
Track the full cost structure of every active engagement, including resource allocation, time spent per workstream, and cost card inputs.
Maintain a running view of cost-to-deliver across the portfolio and surface cost/benefit signals to inform prioritization and go/no-go decisions.
Travel to partner locations during key engagement phases, potentially reaching 50%+ at peak.