Engineering Manager, Catalog

Instacart is the North American leader in online grocery, and we’re building the operating system for the grocery industry so people can access the food they love and more time to enjoy it together.

We’re looking for an Engineering Manager to lead our Catalog Enrichment team. This team builds the AI-native platform and pipelines that create, enrich, and maintain product attributes across a catalog of tens of millions of products from more than 100,000 retailer locations. Catalog data is the foundation of our marketplace: when attributes are complete and accurate, search returns the right results, recommendations feel personal, and advertisers can target with confidence.

In this role, you’ll lead a team of engineers building systems that blend large language models, classical inference, workflow orchestration, and human review into configurable pipelines that non-engineers can run. You’ll also own the ML-facing interfaces to catalog data, including policies that govern how attributes are sourced, shared, and licensed across downstream models and customer surfaces. You will partner closely with ML, product, operations, search, ads, and retailer teams to drive measurable impact across Instacart’s ecosystem.

This is a fast-paced, highly cross-functional environment. You’ll thrive if you enjoy building platforms at scale, turning ambiguity into clarity, and mentoring engineers while rolling up your sleeves on hard technical problems.

• Lead, mentor, and grow a team of engineers who build the platform, pipelines, and interfaces that power catalog enrichment and ML access to catalog data.
• Define and execute the technical roadmap, balancing new platform investments with reliability, observability, and developer experience for a growing user base.
• Build and operate an AI-native enrichment platform that blends LLMs, classical ML, rules, workflow orchestration, and human review into pipelines non-engineers can configure and run.
• Drive the evolution toward autonomous pipeline construction, where users express goals in plain language and the system assembles, evaluates, and optimizes workflows.
• Own the ML-facing catalog data layer, including canonical product metadata and the policy controls that separate internal model inputs from customer-facing use cases.
• Partner with ML, search, ads, commerce, and retailer teams to streamline data flows end to end and remove integration friction.
• Advocate for clear, human-readable controls for source prioritization, rights management, licensing, and compliance to ensure auditable governance.
• Boost developer and operator productivity via AI-assisted integration testing, investigation tooling, and on-call automation; set and meet SLAs on core attributes and model usage.
• Contribute hands-on to architecture discussions, design reviews, and code reviews, especially in new or exploratory areas.