Staff Machine Learning Engineer

Description

The company is hiring a Staff Machine Learning Engineer for its Discovery team to own recommendations and personalization across the consumer site: the homepage feed, product recommendations, search, and the ML systems behind registry building.

The company was built on editorial product picks made by people with deep baby gear expertise, and millions of families trust that foundation. It is now adding ML personalization on top of it, drawing on one of the richest first-party datasets in parenting.

The work is early. The Discovery team has initial retrieval and reranking models live on parts of the site and runs A/B tests regularly. The role comes with a real mandate to set technical direction.

Registry building is central to the product. Parents build lists of dozens of products, from swaddles to strollers, that friends and family buy and a baby uses. Registries often span many retailers, which makes personalization here a hard problem: inferring intent, tracking life-stage changes, handling gift-giving with several people involved, using signals from millions of completed registries and cross-retailer data, and serving users who want help.

This is not a role for tuning models at the margins of a mature ML organization. It suits someone who has worked on a strong recommendations team and now wants to build from zero to one at a company earlier in its ML journey.

The team is small, about 65 engineers, organized in pods of three to five. The company is remote-first across the US and Canada, has been profitable for eight years, and reported more than $750M in revenue for 2025, up 45 percent year over year. Ten million people give gifts through the company each year.

Engineers spend most of their time working with AI agents. The architecture is one Rails monolith with MySQL and few moving parts, which is intentional.

Responsibilities

• Act as technical lead for personalization on the Discovery team, working with a PM, an Engineering Manager, a Senior MLE and fullstack engineers.
• Decide which models to build over the next year or two, set the order of those bets, and keep writing code.
• Own personalization of product recommendations on every surface: product detail pages, add-next suggestions after an item goes into a registry, and vibe or aesthetic representations of products, along with the shared models beneath them.
• Decide what the feed shows each family, covering products, editorial content and social content, and the order they appear in.
• Bring what the company knows about a family into search results, and help decide how far search should share retrieval and ranking with recommendations.
• Improve the AI registry assistant's product suggestions by making it use the same models and signals as the rest of the site.
• Take a vague business problem from first sketch to a production model, and be accountable for whether it moved the target metric.
• Make modeling and architecture decisions that span surfaces and would be costly to undo.
• Design offline and online evaluation that shows whether a model is good before and after launch.
• Judge when a rule or simple baseline is the right first step and when to move to more advanced methods.
• Work with product, design and data to decide what is worth building.
• Coach engineers on the team and serve as the ML expert for the wider engineering organization.

Requirements

• You have shipped recommendation or personalization systems used by real users at scale in a consumer product, and you can point to their business impact.
• You have worked on a team that did ML well and know what good looks like.
• You understand recommender fundamentals, including candidate generation versus ranking, offline versus online evaluation, cold-start handling, and correcting for position and popularity bias.
• You are fluent in the Python ML stack and can own models through deployment and monitoring, including contributing to the application backend.
• You can start on an ambiguous problem before someone hands you the full picture.
• You think like a builder in early-stage ML. You weigh effort against effectiveness and can argue when a rule beats a model or when a model is good enough to learn from.
• You focus on outcomes and users. You measure your work by business impact and can link a model change to user experience, registry adds, conversion or revenue.
• You already use AI daily to work faster and improve your output, and you keep up with what is coming next.

Conditions

For a Canada-based Staff Engineer, the starting base salary range is 299,300 to 372,600 CAD, with a target annual bonus of 20 percent of base. Total target cash is roughly 359,160 to 447,120 CAD.

Equity is meaningful. Where you start in the base range depends on your experience, and pay rises with performance and scope.

The role is remote-first across the US and Canada. The company gets the whole team together in person twice a year.

Teams are pods of three to five engineers. You will work closely with product, design and data, and with partners across the business.

You will also spend regular time with customers, including user interviews, session recordings and riding along with support.

The company expects AI use in the hiring process, but what you submit and say should be your own thinking.

Interviews are recorded and transcribed for evaluation, in line with applicable privacy laws.

If you have a family member or close relationship with an employee, tell your recruiter.

Stack: Rails, Packwerk, React, TypeScript, Sidekiq; iOS (Swift) and Android (Kotlin); MySQL, Snowflake, dbt, Airflow, Weaviate, WarpStream, Hex, Sigma; deep learning, matrix factorization, retrieval and ranking, AWS SageMaker, MLflow; Claude Code, Devin, Linear, CodeRabbit, Warden, LangChain, Bedrock AgentCore, Maxim; AWS, Kubernetes, Terraform, GitHub Actions, Datadog, incident.io, LaunchDarkly; Shopify (payments) and Iterable (CRM) integrations.

Benefits

• Company-paid medical coverage, with 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.
• A paid company-wide week off at the end of each year, called Winter Wonder Week.
• A remote-work stipend.
• Mental-health and wellness support.