Instacart
Engineering Manager, Software, Catalog Enrichment
Vaga remota de Software Engineering com fit claro de localização do candidato.
PublicadaAdicionada recentemente
Países elegíveis1 país aceito
Sinal de senioridadeLead
Modelo de trabalhoRemoto
Locais aceitos para candidatos
Canadá
Resumo da vaga
Engineering Manager, Software, Catalog Enrichment
Requisitos e responsabilidades
Conteúdo da vaga extraído em seções para revisão mais rápida.
About the Job
- 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.
Minimum Qualifications
- 7+ years of software engineering experience, including 2+ years managing engineers as a people leader.
- Experience leading teams that build and operate production data or ML platforms, including both batch and streaming components.
- Strong foundation in distributed systems, data pipelines, and workflow orchestration, with demonstrated build/buy/partner decision-making.
- Ownership of data quality, coverage, or compliance commitments, with examples of frameworks and processes used to meet them.
- Excellent written and verbal communication skills, including the ability to translate complex technical work for diverse audiences.
Preferred Qualifications
- 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.
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