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Added 7 mins ago
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Babylist

Staff Software Engineer, Search

United States

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Job details

Posting language: English
Posted: Nov 2, 2026

Original job description

What the Role Is

We’re hiring a Staff Software Engineer who’ll be the technical lead for a new Search team at Babylist. This new team will be focused on moving Babylist’s search capability in-house, making our search results more relevant and personalized, and partnering with other teams to transform search in the product from functional to delightful.

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 search and personalization problems in consumer e-commerce.

This is zero-to-one work, with big ambitions. You’d build a search capability from the ground up, with a clear mandate from leadership to do it. In your first year, we expect to have rebuilt search to be highly relevant and personalized, and delivered big bets like universal search: one experience across items we sell and items from other retailers, and across products, guides and registries. We also want to work with Addie, our AI registry assistant, to have LLMs play a role in the search experience.

If you want to tune an established search system, this isn’t the right role. If you’ve worked inside a strong search team and want to build a new system from scratch at a company earlier in that journey, read on.

What You'll Own

A Staff Engineer at Babylist sets direction. You’ll be the technical lead for search: you decide where it goes over the next year or two, sequence the bets that get there, and make the technical and product calls along the way. You’re still in the code and will drive initiatives forward across multiple teams. You’ll have software engineering and machine learning engineering support on the team.

A few examples of initiatives you’d drive on the Search team:

Build search in-house. Choose the architecture for indexing, retrieval and serving, and plan a seamless migration. The system you design replaces what we use today and is the foundation for everything below.
Define what a good result is. Decide how we measure result quality, then improve it through query understanding, ranking and the behavioral signals we collect.
Unify search across everything we show. One experience across items we sell and items from other retailers, and across products, guides and registries, including how to rank items we don’t sell next to ones we do.
Decide how search and recommendations fit together. Bring what we know about a user and their registry into results, and decide how much retrieval and ranking search shares with our recommendation models.
Make search conversational. Work with the Addie team, our AI registry assistant, on queries like “best stroller for urban family,” where the shopper describes a need instead of naming a product.

Like every Staff Engineer here, you will:

Take a fuzzy business problem from the first sketch through to production, and stay on the hook for whether it actually helped customers.
Build the platform or tooling other teams adopt by choice, and own it as it scales.
Set the standard for how search builds with AI. Decide what good looks like, build the patterns and evals that get agents there and catch the output that's confidently wrong before it ships.
Partner with product, design and data as a peer, shaping what's worth building from the start.
Coach Senior engineers through the hard calls, the ambiguous ones as much as the technical ones

Who You Are

You meet the Staff bar at Babylist. You’ve shipped production systems for enough years to have earned strong opinions, and you hold them loosely. You can pick up an ambiguous problem and start moving before anyone hands you the full picture. You’ve already changed how a team builds with AI, and the new way stuck.

On top of that, you’ve worked on search problems before, ideally in an e-commerce setting. This role needs depth in the systems behind search and a clear focus on the people using it. You bring:

Hands-on search experience: building and running search in production, including indexing, retrieval, ranking and query understanding. You know how to keep a search service fast, relevant and reliable as traffic and catalog size grow.
Experience evaluating search quality: offline and online, including relevance judgments and A/B tests.
A focus on the user: you use query data, behavior and judgment about result quality to decide what to build, and you can connect a search change to what shoppers do, and to conversion or revenue.
Comfort building from zero: you can define the problem, design the system and own it end to end.
You measure yourself by impact: a customer outcome, or a system a dozen teams come to depend on.

Compensation

We post real numbers. For a Canada-based Staff Engineer, the starting base salary range is $299,300 to $372,600 CAD, plus a target annual bonus of 20 percent of base. That's total target cash of roughly $359,160 to $447,120 CAD. On top of that you get meaningful equity and an RRSP match. Where you start in that range depends on your experience, and your pay grows from there with performance and scope.

How We Build

AI is the default here. Engineers run agentic sessions for most of the work, and a lot of the interesting engineering now lives in the scaffolding that makes the agents good: the eval harnesses, the curated context, custom review skills and fast CI. Agents also triage incidents and handle a big share of support. A human always owns the outcome.

The architecture is intentionally simple: one Rails monolith, MySQL and few moving parts. That's deliberate. Simple infrastructure lets us move fast and lets AI reason about the whole system, so the hardest problems are the ones in front of customers.

The Stack

Core app: Rails, Packwerk, React, TypeScript, Sidekiq
Mobile: iOS (Swift), Android (Kotlin)
Data, search & events: MySQL, Snowflake, dbt, Airflow, Weaviate, WarpStream, Hex, Sigma
Machine learning: deep learning, matrix factorization, retrieval & ranking, AWS SageMaker, MLflow
AI & dev tooling: Claude Code, Devin, Linear, CodeRabbit, Warden, LangChain, Bedrock AgentCore, Maxim
Infra & ops: AWS, Kubernetes, Terraform, GitHub Actions, Datadog, incident.io, LaunchDarkly
Key integrations: Shopify (payments), Iterable (CRM)

Why Babylist

An engineer, expecting her first baby, couldn't find the registry she wanted. So she built it. That's how Babylist started, and it's still how we work: engineers solving problems for families. Becoming a parent is one of the biggest moments in a person's life. Millions reach it for the first time every year, making thousands of decisions and figuring it out as they go. That's who we build for, and we're a long way from done.

Ten million people give gifts through Babylist every year. We did more than $750M in revenue in 2025, up 45 percent over the year before, and we've been profitable for eight years while staying independent. So you can take on a hard, multi-year problem without watching over your shoulder for the next round or the next correction. And the team is small, around 65 engineers, so what you ship stays visible and your scope stays wide.

How We Work

Remote-first across the US and Canada, 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.

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

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