Staff Machine Learning Engineer

Babylist is hiring a Staff Machine Learning Engineer to lead personalization for its Discovery team. The role sets technical direction and builds recommendation, feed, and search systems that improve the consumer experience and registry building.

Responsibilities:

• Product recommendations across every surface. Product detail pages, add-next recommendations after someone adds to their registry, vibes/aesthetic representations of products and the shared models underneath them
• Personalizing the Feed. Deciding which products, editorial content and social content each family sees, and in what order
• Personalizing search. Bringing what we know about a family into search results, and helping decide how much search should share retrieval and ranking with recommendations
• Making Addie (our AI registry assistant) better at recs. Working with the team behind Addie so its product suggestions draw on the same models and signals as the rest of the site
• Take a fuzzy business problem from first sketch to production model, and own whether it moved the metric
• Make the modeling and architecture calls that span surfaces and are expensive to reverse
• Design the evaluation, offline and online, that tells us a model is good before and after it ships
• Know when a rule or a simple baseline is the right first step, and when it's time to pursue more advanced approaches
• Partner with product, design and data to help shape what's worth building
• Coach engineers on the team, and more broadly be an ML expert and resource to the wider engineering org

Requirements:

• You've shipped recommendation and/or personalization systems that reached real users at scale within a consumer product, and can point to the business impact of your work
• You've done this within a team that did ML well — you know what good looks like, and are motivated to bring that to a company earlier in their ML journey
• Expertise in recommender fundamentals: for example, candidate generation vs. ranking, offline vs. online evaluation, cold start solutions, and handling position and popularity bias
• Fluency in the Python ML stack and comfort owning models through deployment and monitoring, including contributions to our application backend
• An ability to take an ambiguous problem and start moving before anyone hands you the full picture
• A builder's instinct for early-stage ML. You understand effort vs. effectiveness tradeoffs and can advocate for when a rule beats a model, or a model is “good enough” for early learnings
• An outcome and user focused mindset. You measure yourself by business impact, and you can connect a model change to user experience & registry adds, conversion or revenue
• AI-native daily practice. You're already using AI to move faster and improve your output, and you stay curious about what's coming next