Staff Machine Learning Engineer

The posting, as Babylist wrote it

What The Role Is

We're hiring a Staff MLE for the Discovery team to own recommendations and personalization across Babylist's consumer experience — the homepage feed, product recs, search, and the ML-powered systems that make registry building feel effortless.

Babylist was built on editorial recommendations: products chosen by people with deep baby gear expertise. That editorial foundation is a big part of why millions of families trust us. We're now building ML-powered personalization on top of it, using one of the richest first-party datasets in parenting.

We're early in this work, and we have a real mandate. A small Discovery team has initial retrieval and reranking models live on parts of the site and a steady cadence of A/B tests. We are looking for a Staff MLE who has seen personalization done well at scale and can set the technical direction for where we go next.

Registry building is the heart of the Babylist product. Every parent builds a list of dozens of products, from swaddle to stroller, with real stakes (a friend or family member is going to buy these things, and a baby is going to use them). As a universal registry, this registry also typically spans many retailers. This makes registry building on Babylist one of the most interesting personalization problems in consumer e-commerce: latent intent, life-stage progression, multi-stakeholder gift dynamics, deep declarative signal in millions of completed registries, cross-retailer datapoints, and a user who genuinely wants help.

If you want to join a mature ML org and tune models at the margins, this isn't the right role. If you've worked inside a strong recommendations team, learned what good looks like, and want to build from zero-to-one at a company earlier in the journey, read on.

What You'll Own

You'll be the technical lead for personalization on the Discovery team, working alongside a PM, Engineering Manager, Senior MLE and fullstack software engineers. You set where our models go over the next year or two, sequence the bets that get there, and stay deep in building. A few examples of the problems you’ll get to shape:

• 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.

In practice, you will

• Take a fuzzy business problem from first sketch to production model, and own whether it moved the metric.
• Make the modeling and architec

[…]
da, and we have been for years. That's not changing. We trust you to own your time and your outcomes, and we get everyone in a room together twice a year. Teams are small, pods of three to five engineers, so nothing you ship disappears into a committee. You'll work shoulder to shoulder with product, design and data, and with the partners across the business who rely on what you ship. You'll also stay close to customers yourself: sitting in on user interviews, watching session recordings, riding along with support. Here that's part of the engineering job, on a regular basis.

How We Hire

Three rounds, usually two to three weeks start to finish.

• Recruiter conversation (30 minutes). Trade context: what you want next, what we're building and straight answers on comp, team and remote.
• Technical screen (1 hour). One round, no AI, language-agnostic. Reach for Google when you normally would. We just want to see how you reason from first principles.
• Final round (4 hours). Four one-hour interviews: system design, AI-assisted coding with the tools you'd actually use here, product sense and culture and values. You'll hear back either way within 24 to 48 hours.

If your timeline is tight, tell us and we'll move faster.

Benefits

• Company-paid medical and fully covered dental and vision
• A 401(k) match
• Generous paid parental leave for birthing and non-birthing parents, plus a gradual return-to-work program
• Winter Wonder Week, a paid company-wide week off at the end of the year
• A remote-work stipend
• Mental-health and wellness support

A Few Things To Know

• We record and transcribe interviews to evaluate candidates, in line with applicable privacy laws.
• We expect you to use AI in your work and we welcome it in the process, but what you submit and say should reflect your own thinking.
• If you have a family member or close relationship with a Babylist employee, let your recruiter know.
• Official outreach only ever comes from an @babylist.com address