Staff Software Engineer, Search
New
B
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
- Experience
- You’ve shipped production systems for enough years to have earned strong opinions, and you hold them loosely.
- Required Skills
- Artificial IntelligenceMachine LearningA/B testing
Requirements
- Meet Babylist’s Staff Engineer bar and have shipped production systems.
- Have hands-on experience building and running production search systems, including indexing, retrieval, ranking and query understanding.
- Know how to keep search services 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, behavior and result-quality judgment to guide search work.
- Connect search changes to shopper behavior and conversion or revenue.
- Be able to define ambiguous problems, design systems and own them end to end.
- Have changed how a team builds with AI in a way that was adopted and sustained.
- Have worked on search problems; e-commerce experience is preferred.
Responsibilities
- Set the technical direction for search and sequence the team’s initiatives.
- Choose the architecture for indexing, retrieval and serving, and plan a seamless migration to an in-house system.
- Define search quality measures and improve results through query understanding, ranking and behavioral signals.
- Unify search across Babylist products, products from other retailers, guides and registries.
- Determine how search and recommendation models share retrieval and ranking, incorporating user and registry information into results.
- Partner with the Addie team to develop conversational search experiences.
- Take ambiguous business problems from initial concept through production and assess their customer impact.
- Build and own platforms or tooling adopted by other teams.
- Establish patterns and evaluations for AI-assisted search development and identify incorrect agent output before release.
- Partner with product, design and data, and coach senior engineers through technical and ambiguous decisions.
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