Staff Machine Learning Engineer

Babylist

. Own recommendations and personalization across Babylist’s consumer experience, including the homepage feed, product recommendations, search, and ML-powered systems .

Posted 11/3/2026full-timeRemote • United States, CanadaLead💰 $233,500 - $290,700 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and deploying recommendation and personalization systems, with a strong focus on user impact and business metrics. Proficient in Python ML stack and capable of leading technical direction in machine learning applications.

Highest-signal resume keywords

Recommendation Systems DevelopmentPersonalization ExpertisePython ML Stack FluencyMachine Learning Model DeploymentBusiness Impact Analysis

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Recommendation SystemsPersonalization SystemsMachine Learning FundamentalsCandidate GenerationRanking TechniquesOffline EvaluationOnline EvaluationCold Start SolutionsModel MonitoringApplication Backend Contributions

Soft Skills

Curiosity About AI DevelopmentsProblem-SolvingTeam CollaborationCoaching EngineersUser-Focused Mindset

Industry Keywords

Consumer ProductAI Registry AssistantProduct RecommendationsFeed PersonalizationSearch Personalization

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

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