Wizardcommerce

Wizardcommerce

Machine Learning Engineer - Relevance & Learning Systems

Remote - USA

Sponsorship not specified$225k-$280kDetected 120 days ago
PythonMachine LearningResearch

About the role

  • You'll be working at the intersection of a live conversational agent and real shopping behavior - the feedback signal quality here is unusually rich compared to traditional search.
  • You'll focus on turning user interactions into learning signals, designing practical feedback loops and shipping systems that continuously improve real world outcomes.

Responsibilities

  • Build and productionize feedback loops that improve agent performance over time
  • Build the evaluation infrastructure - offline metrics, regression suites, and experiment analysis
  • Partner closely with product and engineering to define success metrics and optimize for them
  • You ship quickly and drive measurable improvements in core product metrics
  • You own systems end to end and operate comfortably in production
  • 5-8 years hands on experience building and shipping ML systems

Requirements

  • Bachelor's or Master's degree in computer science
  • Experience shipping ML systems to production and have worked on recommendation systems, ranking, personalization or optimization problems

Compensation

  • Fully remote work within the United States
  • Periodic company offsites and team gatherings
  • The expected base salary range for this role is $225,000 - $280,000 USD, and will vary based on skills, experience, role level, and geographic location.
  • Final compensation will be determined by considering these factors alongside overall role scope and responsibilities.
  • In addition to base salary, Wizard offers:
  • Wizard is committed to fair, transparent, and competitive compensation practices.

Benefits

  • Own the signal pipelines end-to-end: instrument events, build clean labeled datasets, and translate user behaviors into reliable learning signals
  • Design lightweight reinforcement learning / bandit-style approaches where appropriate
  • Design and analyze experiments that validate whether learning system changes actually improve real outcomes
  • You turn noisy user behavior into reliable learning signals that improve the agent over time
  • Equity in the form of stock options
  • Medical, dental, and vision coverage
  • Flexible PTO and company holidays

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