Machine Learning Engineer - Relevance & Learning Systems
About Wizard
Wizard is the top-performing AI Shopping Agent, delivering the best products from across the web with unmatched accuracy, quality, and trust.
The Role
We’re looking for a Machine Learning Engineer to design and build feedback driven learning systems that improve our AI agent over time. This is not a traditional RL research role, we’re focused on building systems that learn from real user behavior and improve production. 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.
What You’ll Do
Build and productionize feedback loops that improve agent performance over time
Build the evaluation infrastructure – offline metrics, regression suites, and experiment analysis
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
Partner closely with product and engineering to define success metrics and optimize for them
Design and analyze experiments that validate whether learning system changes actually improve real outcomes
Improve ranking, recommendations and decision making within the agent
Iterate quickly: Ship → measure → learn → improve
What Success Looks like
You ship quickly and drive measurable improvements in core product metrics
You turn noisy user behavior into reliable learning signals that improve the agent over time
You own systems end to end and operate comfortably in production
Ideal Background
5-8 years hands on experience building and shipping ML systems
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
Deep knowledge in Python and model ML tooling
Pragmatic: you choose simple, effective solutions over theoretically perfect ones
Compensation & Benefits
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:
Equity in the form of stock options
Medical, dental, and vision coverage
401(k) plan
Flexible PTO and company holidays
Fully remote work within the United States
Periodic company offsites and team gatherings
Wizard is committed to fair, transparent, and competitive compensation practices.
Apply Now
Wizard is the top-performing AI Shopping Agent, delivering the best products from across the web with unmatched accuracy, quality, and trust.
The Role
We’re looking for a Machine Learning Engineer to design and build feedback driven learning systems that improve our AI agent over time. This is not a traditional RL research role, we’re focused on building systems that learn from real user behavior and improve production. 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.
What You’ll Do
Build and productionize feedback loops that improve agent performance over time
Build the evaluation infrastructure – offline metrics, regression suites, and experiment analysis
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
Partner closely with product and engineering to define success metrics and optimize for them
Design and analyze experiments that validate whether learning system changes actually improve real outcomes
Improve ranking, recommendations and decision making within the agent
Iterate quickly: Ship → measure → learn → improve
What Success Looks like
You ship quickly and drive measurable improvements in core product metrics
You turn noisy user behavior into reliable learning signals that improve the agent over time
You own systems end to end and operate comfortably in production
Ideal Background
5-8 years hands on experience building and shipping ML systems
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
Deep knowledge in Python and model ML tooling
Pragmatic: you choose simple, effective solutions over theoretically perfect ones
Compensation & Benefits
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:
Equity in the form of stock options
Medical, dental, and vision coverage
401(k) plan
Flexible PTO and company holidays
Fully remote work within the United States
Periodic company offsites and team gatherings
Wizard is committed to fair, transparent, and competitive compensation practices.
Apply Now