Staff Machine Learning Engineer

🔥 13 hours ago

🌐 Canada, United States – Remote

🇨🇦 Canada – Remote 🇺🇸 United States – Remote

💵 $299.3k - $372.6k / year

⏰ Full Time

🔴 Lead

🤖 Machine Learning Engineer

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Babylist

WebsiteLinkedInAll Job Openings

51 - 200 employees

🛍️ eCommerce

🛒 Retail

💰 $40M Series C on 2021-11

eCommerce • Retail

Babylist is the leading vertical marketplace and commerce destination for baby products, driving purchase decisions for more than 8 million people each year. It utilizes robust proprietary data, patented technology, and unbiased editorial guidance to recommend expert-tested products to those starting their parenting journey. Babylist registries connect new parents with their community of family and friends who help plan, prepare, and shop for a child's arrival. Founded in 2011 by Natalie Gordon, Babylist has grown from a side project into a generational brand in the $67 billion baby products market.

📋 Description

• Own recommendations and personalization across Babylist's consumer experience, including the homepage feed, product recommendations, search, and ML-powered systems • Set technical direction for personalization over the next one to two years • Lead product recommendations across product detail pages, registry add-next recommendations, and product aesthetic representations • Personalize the Feed's product, editorial, and social content selection and ordering • Personalize search using family signals and align search retrieval and ranking with recommendations • Improve product suggestions for Addie, Babylist's AI registry assistant • Take ambiguous business problems from initial concept through production models and own their business impact • Make modeling and architecture decisions spanning multiple product surfaces • Design offline and online model evaluation before and after launch • Determine when rules or simple baselines are preferable to advanced models • Partner with product, design, and data on priorities • Coach team engineers and serve as an ML expert across the engineering organization

🎯 Requirements

• Shipped recommendation and/or personalization systems to real users at scale within a consumer product • Demonstrated business impact from recommendation or personalization work • Expertise in recommender fundamentals, including candidate generation vs. ranking, offline vs. online evaluation, cold start solutions, and handling position and popularity bias • Fluency in the Python ML stack • Comfort owning models through deployment and monitoring • Contributions to an application backend • Ability to work through ambiguous problems independently • Understanding of effort-versus-effectiveness tradeoffs in early-stage ML • Outcome- and user-focused mindset connecting model changes to user experience, registry adds, conversion, or revenue • Daily use of AI tools and curiosity about emerging AI capabilities

🏖️ Benefits

• Target annual bonus of 20 percent of base • Meaningful equity • A 401(k) match • Company-paid medical • Fully covered dental and vision • Generous paid parental leave for birthing and non-birthing parents • Gradual return-to-work program • Winter Wonder Week, a paid company-wide week off at the end of the year • Remote-work stipend • Mental-health and wellness support • Remote-first work across the US and Canada • Twice-yearly company-wide in-person gatherings

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