Machine Learning Engineer I

About this role

Gen seeks a hands-on Machine Learning Engineer I to build and deploy ML models driving customer growth, retention, and personalization across their cybersecurity and financial wellness platforms. You'll own end-to-end projects—from data exploration through production deployment—collaborating with engineering, product, and analytics teams while leveraging AI tools to accelerate your workflow.

What you'll do

• Own machine learning projects from data exploration and model development through validation, deployment, and iteration
• Build predictive, recommendation, ranking, segmentation, and uplift models for customer personalization and decisioning
• Prepare datasets, define targets, engineer features, and ensure data quality for training and evaluation
• Design and analyze A/B tests, holdouts, and offline evaluations to measure model performance and business impact
• Collaborate with engineering, product, and business partners to integrate models into production and refine based on feedback
• Use AI coding assistants and automation to improve speed, quality, and consistency of modeling workflows

What they're looking for

• Python programming and machine learning
• Supervised learning, model selection, hyperparameter tuning, and evaluation
• SQL and big data platforms (BigQuery, Spark, or similar)
• Data exploration, cleaning, preprocessing, and feature engineering
• A/B testing design and statistical analysis
• Experimentation, metrics definition, and business impact measurement
• Recommender systems, uplift modeling, or causal inference (preferred)
• Cross-functional collaboration and communication

Benefits

• Flexible working options and work-from-home policies
• Competitive pay and comprehensive benefits package
• Well-being programs and professional development support
• Opportunity to work on high-impact AI solutions for 500M+ users globally
• Collaborative team culture with emphasis on healthy debate and continuous learning
• Access to cutting-edge AI tools and coding assistants

Likely interview questions

• Walk us through a machine learning project you owned end-to-end—what was the business problem, how did you approach feature engineering, and how did you measure success?
• Describe your experience designing or analyzing A/B tests to evaluate model performance. What was a surprising result or learning?