Staff Software Engineer, Search
New
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BabylistE-commerce search
Remote-first across the US and CanadaFull-TimeStaff
SalaryFor a US-based Staff Engineer, the starting base salary range is $233,500 to $290,700, plus a target annual bonus of 20 percent of base. That's total target cash of roughly $280,200 to $348,840. On top of that you get meaningful equity and a 401(k) match.
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Job Details
- Required Skills
- Artificial IntelligenceMachine LearningA/B testing
Requirements
- Have hands-on experience building and running search in production.
- Have experience with search indexing, retrieval, ranking, and query understanding.
- Know how to keep a search service fast, relevant, and reliable as traffic and catalog size grow.
- Have experience evaluating search quality offline and online, including relevance judgments and A/B tests.
- Use query data, user behavior, and result-quality judgment to decide what to build.
- Be able to connect search changes to shopper behavior and conversion or revenue.
- Be comfortable defining an ambiguous problem, designing a system, and owning it end to end.
- Have shipped production systems and changed how a team builds with AI in a way that persisted.
- Be able to partner with product, design, and data as a peer and coach senior engineers.
Responsibilities
- Set the technical direction for Babylist search and sequence the work over the next year or two.
- Choose the architecture for indexing, retrieval, and serving, and plan a seamless migration to an in-house system.
- Define how search-result quality is measured and improve it through query understanding, ranking, and behavioral signals.
- Build a unified search experience across Babylist products, products from other retailers, guides, and registries.
- Connect search results with user and registry information, and determine how search retrieval and ranking should work with recommendation models.
- Partner with the Addie team on conversational search and queries that describe a shopper’s needs.
- Take ambiguous business problems from initial concept through production and assess their customer outcomes.
- Build and own platforms or tools that other teams adopt, including patterns and evaluations for building with AI.
- Partner with product, design, and data, and coach senior engineers through technical and ambiguous decisions.
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