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 OwnYou'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 architecture calls that span surfaces and are expensive to reverse.
- Design the evaluation, offline and online, that tells us a model is good before and after it ships.
- Know when a rule or a simple baseline is the right first step, and when it's time to pursue more advanced approaches.
- Partner with product, design and data to help shape what's worth building.
- Coach engineers on the team, and more broadly be an ML expert and resource to the wider engineering org.
You’ve shipped recommendation and/or personalization systems that reached real users at scale within a consumer product, and can point to the business impact of your work. You’ve done this within a team that did ML well — you know what good looks like, and are motivated to bring that to a company earlier in their ML journey.
You bring:
- Expertise in recommender fundamentals: for example, candidate generation vs. ranking, offline vs. online evaluation, cold start solutions, and handling position and popularity bias.
- Fluency in the Python ML stack and comfort owning models through deployment and monitoring, including contributions to our application backend.
- An ability to take an ambiguous problem and start moving before anyone hands you the full picture.
- A builder's instinct for early-stage ML. You understand effort vs. effectiveness tradeoffs and can advocate for when a rule beats a model, or a model is “good enough” for early learnings.
- An outcome and user focused mindset. You measure yourself by business impact, and you can connect a model change to user experience & registry adds, conversion or revenue.
- AI-native daily practice. You're already using AI to move faster and improve your output, and you stay curious about what's coming next.
We post real numbers. For a US-based Staff Engineer, the starting base salary range is $233,500 to $290,700, plus a target annual bonus of 20 percent of base. That's total target cash of roughly $280,200 to $348,840. On top of that you get meaningful equity and a 401(k) 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)
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 WorkRemote-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 HireThree 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
- 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
