Staff ML Engineer: Real-Time Rewards Personalization

Mistplay; Los Angeles (CA); On-site; USD 180,000 - 260,000; Full time

Benefits offered by this job

Team lunches

Game nights

Company-wide events

Job summary

Mistplay is seeking a Staff Machine Learning Engineer to lead the design and optimization of our rewards economy. You will develop and balance reward systems for enticing user engagement while maintaining cost-effectiveness, collaborating with Data and AI to advance real-time predictive modeling and ML-driven operations.

You'll drive ML-driven rewards architectures, contextual bandits, and RL frameworks, translating business goals into algorithms and incentives with cross-functional partners.

Qualifications

• 8+ years in data science, machine learning, econometrics or related quantitative field.
• Understanding of reinforcement learning (DeepQ, contextual bandits).
• Experience implementing ML models with Python and PyTorch; strong data analysis with SQL and Python.

Responsibilities

• Design and optimize Mistplay's rewards economy and core loops using ML.
• Balance reward systems to maximize user engagement and unit economics.
• Develop real-time predictive modeling and optimization within ML-driven operations.
• Translate business goals into algorithmic constraints and incentive structures.
• Collaborate with Product, Analytics, Engineering, and Finance to implement AI-driven loyalty features.

Skills

Reinforcement learning

Python

PyTorch

SQL

Data analysis

Education

Master's or PhD in Data Science/ML or related quantitative field

Tools

DeepQ

Contextual bandits

Job description

Mistplay is seeking a Staff Machine Learning Engineer to lead the design and optimization of our rewards economy. You will develop and balance reward systems for enticing user engagement while maintaining cost-effectiveness, collaborating with Data and AI to advance real-time predictive modeling and ML-driven operations.

You'll drive ML-driven rewards architectures, contextual bandits, and RL frameworks, translating business goals into algorithms and incentives with cross-functional partners.