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

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

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningreinforcement learningPythonrecommendation systemsrankingpersonalizationoptimizationexperiment analysisdata pipelinesmetrics

Soft Skills

pragmatic problem solvingcollaborationcommunicationiterationanalytical thinking

Certifications

Bachelor's degree in computer scienceMaster's degree in computer science