Faire

Faire

Senior Applied ML/AI Scientist - Search

Kitchener-Waterloo, ON; Toronto, ON · Senior

Sponsorship not specified$171k-$236kDetected 1 day ago
PythonAlgorithmsMachine LearningNLPLLMsMLOpsStatisticsCommunication

About the role

  • Search is how retailers do their jobs on Faire.
  • Wholesale queries and retailer expectations look different from consumer e-commerce and the right product depends on the store's category, price point, and aesthetic.
  • When we get it wrong, it costs real money.

Responsibilities

  • 3+ years building production ML systems, with meaningful time in search, recommendations, or another retrieval-and-relevance domain.
  • Strong Python and the engineering chops to take your own models to production.
  • when you own query understanding here, you own the models, the roadmap, and the metrics.
  • Within our scope, scientists own components outright: when you own query understanding here, you own the models, the roadmap, and the metrics.
  • Move fast: You'll own meaningful problems that serve customers around the globe with the agency to move fast and see your results clearly.

Skills

  • Faire is a technology wholesale platform built on the belief that the future is local.
  • Best in

Compensation

  • Canada: the pay range for this role is $171,000 to $235,500 per year.
  • Actual base pay will be determined based on permissible factors such as transferable skills, work experience, market demands, and primary work location.
  • The base pay range provided is subject to change and may be modified in the future.
  • Additionally, hybrid in-office roles will have the flexibility to work remotely up to 4 weeks per year.
  • the pay range for this role is $171,000 to $235,500 per year.

Benefits

  • This role will also be eligible for equity and benefits.

Company info

  • You'll own meaningful problems that serve customers around the globe with the agency to move fast and see your results clearly.

This listing is sourced directly from Faire's careers page and normalized into a canonical job model.