Engineering Manager, Software

Instacart Instacart · Consumer · Canada · Remote · Software Engineering

Engineering Manager to lead the Catalog Enrichment team, building an AI-native platform and pipelines that create, enrich, and maintain product attributes. The role involves blending LLMs, classical inference, workflow orchestration, and human review into configurable pipelines, owning ML-facing interfaces, and partnering with various teams to drive impact. The goal is to evolve towards autonomous pipeline construction and ensure auditable governance.

What you'd actually do

  1. 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.
  2. Define and execute the technical roadmap, balancing new platform investments with reliability, observability, and developer experience for a growing user base.
  3. 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.
  4. Drive the evolution toward autonomous pipeline construction, where users express goals in plain language and the system assembles, evaluates, and optimizes workflows.
  5. 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.

Skills

Required

  • 7+ years of software engineering experience
  • 2+ years managing engineers as a people leader
  • Experience leading teams that build and operate production data or ML platforms
  • Strong foundation in distributed systems, data pipelines, and workflow orchestration
  • Ownership of data quality, coverage, or compliance commitments
  • Excellent written and verbal communication skills

Nice to have

  • Experience with catalog, commerce, or product data platforms operating at large scale
  • Hands-on experience with workflow orchestration systems (e.g., Temporal, Airflow, Flink) and running them in production
  • Experience shipping applied AI/ML features in production and understanding trade-offs across LLMs, classical ML, deterministic rules, and human review
  • Proven success partnering across ML, product, and operations to deliver outcomes that require alignment and shared goals
  • Familiarity with data governance, rights management, and licensing controls in multi-stakeholder environments
  • Experience building pipeline builders, low-code/no-code tooling, or internal platforms used by non-engineers
  • Background in developer productivity tooling, including AI-assisted testing and investigation workflows
  • Success operating in fast-growing environments where both the platform and team are evolving

What the JD emphasized

  • AI-native enrichment platform
  • autonomous pipeline construction
  • ML-facing catalog data layer
  • workflow orchestration
  • human review
  • LLMs
  • classical ML
  • large scale
  • production data or ML platforms
  • data quality, coverage, or compliance commitments

Other signals

  • AI-native platform
  • LLMs
  • workflow orchestration
  • human review
  • ML-facing interfaces
  • autonomous pipeline construction
  • data governance
  • developer productivity