Staff Machine Learning Engineer

This is a fully remote position, open to applicants in United States, +1 more country.

📋 Description

• Take charge of recommendations and personalization throughout Babylist’s consumer experience, which includes the homepage feed, product recommendations, search functionalities, and machine learning-powered systems.

• Establish the technical roadmap for personalization for the upcoming year or two.

• Lead the technical aspects of personalization within the Discovery team, collaborating with product, engineering, and full-stack partners.

• Influence product recommendations on product detail pages, registry add-next suggestions, and aesthetic representations of products.

• Tailor the Feed’s selection and ordering of product, editorial, and social content.

• Customize search functionality by utilizing family signals and determining shared retrieval and ranking methodologies.

• Enhance Addie, Babylist’s AI registry assistant, by synchronizing product suggestions with shared models and signals.

• Navigate ambiguous business challenges from the initial concept through to production models while owning the metric impact.

• Make decisions regarding modeling and architecture that span across multiple product surfaces.

• Design both offline and online evaluation processes for pre- and post-launch model assessments.

• Appropriately choose between rules, simple baselines, and advanced methodologies.

• Collaborate with product, design, and data teams on prioritization efforts.

• Mentor team engineers and act as a machine learning expert throughout the engineering organization.

⛳️ Requirements

• Successfully launched recommendation and/or personalization systems that have reached real users at scale within a consumer product.

• Proven ability to demonstrate the business impact of recommendation or personalization efforts.

• Experience working within a team that has excelled in machine learning.

• Deep understanding of recommender system fundamentals, including candidate generation versus ranking, offline versus online evaluation, cold start solutions, and managing position and popularity biases.

• Proficiency in the Python machine learning stack.

• Comfort in managing models throughout deployment and monitoring phases.

• Contributions to an application backend are necessary.

• Capability to tackle ambiguous problems and initiate progress without having a complete picture.

• Awareness of the trade-offs between effort and effectiveness in early-stage machine learning.

• Outcome- and user-driven mindset that connects model changes to user experience, registry additions, conversion rates, or revenue.

• Regular utilization of AI to enhance speed and output.

• A genuine curiosity about emerging developments in AI.

🏝️ Benefits

• Target annual bonus of 20 percent of base salary.

• Meaningful equity participation.

• A 401(k) matching program.

• Company-paid medical insurance and fully covered dental and vision plans.

• Generous paid parental leave for both 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.

• Support for mental health and wellness.