Staff Machine Learning Engineer

🔥 17 hours ago

🌐 United States, Canada – Remote

🇺🇸 United States – Remote 🇨🇦 Canada – Remote

💵 $233.5k - $290.7k / year

⏰ Full Time

🔴 Lead

🤖 Machine Learning Engineer

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This company has sponsored H1B visas in the past.

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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 the technical direction for personalization over the next year or two • Lead personalization technically on the Discovery team alongside product, engineering, and fullstack partners • Shape product recommendations across product detail pages, registry add-next recommendations, and product vibe/aesthetic representations • Personalize the Feed’s product, editorial, and social content selection and ordering • Personalize search using family signals and determine shared retrieval and ranking approaches • Improve Addie, Babylist’s AI registry assistant, by aligning product suggestions with shared models and signals • Take ambiguous business problems from initial concept through production models and own metric impact • Make modeling and architecture decisions spanning multiple product surfaces • Design offline and online evaluation for pre- and post-launch model assessment • Choose appropriately between rules, simple baselines, and advanced approaches • Partner with product, design, and data on prioritization • Coach team engineers and serve as an ML expert across the engineering organization

🎯 Requirements

• Shipped recommendation and/or personalization systems that reached real users at scale within a consumer product • Ability to demonstrate business impact of recommendation or personalization work • Experience working within a team that did ML well • 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 take ambiguous problems and start moving without a complete picture • 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 to improve speed and output • Curiosity about emerging AI developments

🏖️ Benefits

• Target annual bonus of 20 percent of base • Meaningful equity • A 401(k) match • Company-paid medical and 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

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