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 • Canada, United StatesLead💰 CA$299,300 - CA$372,600 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 experience and business impact. Proficient in Python ML stack and capable of leading technical direction and model evaluation in a consumer product environment.

Highest-signal resume keywords

Recommendation Systems DevelopmentPersonalization ExpertisePython ML Stack FluencyModel Deployment and MonitoringBusiness Impact Analysis

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Recommendation FundamentalsCandidate GenerationRanking TechniquesOffline EvaluationOnline EvaluationCold Start SolutionsPosition Bias HandlingPopularity Bias HandlingApplication Backend ContributionsAI Tools Utilization

Soft Skills

Problem-SolvingIndependent WorkCoaching and Mentoring

Industry Keywords

Consumer ProductMachine LearningPersonalization SystemsUser ExperienceBusiness Impact

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 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

What you’ll need

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

Comp & perks

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