Staff Machine Learning Engineer
New
B
BabylistConsumer e-commerce
Remote-first across the US and CanadaFull-TimeStaff
SalaryThe 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.
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Job Details
- Required Skills
- PythonMachine Learning
Requirements
- Have shipped recommendation and/or personalization systems to real users at scale within a consumer product.
- Be able to point to the business impact of your recommendation or personalization work.
- Bring experience from a team with strong machine learning practices.
- Demonstrate expertise in recommender fundamentals, including candidate generation versus ranking.
- Understand offline and online evaluation, cold-start solutions, and handling position and popularity bias.
- Be fluent in the Python ML stack and comfortable owning models through deployment and monitoring.
- Be comfortable contributing to the application backend.
- Be able to make progress on ambiguous problems without needing a complete specification.
- Understand effort-versus-effectiveness tradeoffs and when to use a rule, baseline, or more advanced model.
- Connect model changes to user experience and outcomes such as registry adds, conversion, or revenue.
- Use AI in daily work to improve speed and output.
Responsibilities
- Set the technical direction and sequence the work for personalization on the Discovery team.
- Build product recommendations across product detail pages and registry-add flows, including shared models for product representations.
- Personalize the feed by deciding which products, editorial content, and social content families see and in what order.
- Personalize search results using information about each family and determine how search shares retrieval and ranking with recommendations.
- Work with the Addie team to align its product suggestions with the site's models and signals.
- Take ambiguous business problems from initial sketch through production models and own whether they move the metric.
- Make modeling and architecture decisions spanning surfaces, and design offline and online evaluations.
- Partner with product, design, and data to shape what to build.
- Coach team engineers and serve as an ML resource to the wider engineering organization.
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