Machine Learning Engineer - Feedback & Learning Systems

Wizard

4h•$225,000 - $280,000•Remote

About The Position

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.

Requirements

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

Responsibilities

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