Wizard - Machine Learning Engineer - Relevance & Learning Systems

• 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 • 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