Applied ML Engineer II – Personalization & Growth

Gen; Mountain View (CA); On-site; USD 255,000 - 345,000; Full time

Job summary

Gen is seeking a hands-on AI/ML Engineer to contribute to our AI transformation. You will build practical models that personalize customer decisions across in-app messages, emails, and lifecycle journeys, and collaborate with engineering and product teams to bring models into production.

The role focuses on applied machine learning, experimentation, and business-impact modeling, with experience in recommender systems, uplift modeling, contextual bandits, pricing, or lifecycle personalization as

Qualifications

• Bachelor's degree in CS/DS/Math/Engineering or related field; equivalent practical experience valued.
• Five+ years of applied ML, data science, ML engineering, or related field with demonstrated impact.
• Experience with large-scale data and experimentation design; ability to translate results into product decisions.

Responsibilities

• End-to-end ML ownership from data prep to production deployment and monitoring.
• Productionize ML solutions with scalable pipelines and MLOps workflows.
• Design and analyze A/B tests, holdouts, and validation frameworks.
• Develop propensity, uplift, recommendation and ranking models; support contextual bandits and lifecycle personalization.
• Collaborate with ML infrastructure, data engineering, backend, product and analytics teams to deploy models in production.
• Build tooling to streamline model development and MLOps workflows.

Skills

Python

ML frameworks

SQL

BigQuery

Spark

A/B testing

Statistical reasoning

MLOps

Model evaluation

Cloud deployment

Education

Bachelor's degree in related field

Master's or PhD (bonus)

Tools

CI/CD tools

Model registries

Cloud platforms

Job description

Gen is seeking a hands-on AI/ML Engineer to contribute to our AI transformation. You will build practical models that personalize customer decisions across in-app messages, emails, and lifecycle journeys, and collaborate with engineering and product teams to bring models into production.

The role focuses on applied machine learning, experimentation, and business-impact modeling, with experience in recommender systems, uplift modeling, contextual bandits, pricing, or lifecycle personalization as