Machine Learning Engineer: Personalization & Ranking at Scale

SuprAIJobs; San Jose (CA); On-site; USD 70,000 - 170,000; Full time

Benefits offered by this job

Flexible work environment

Employee stock options

Health and life insurance

Job summary

PayPal is seeking a talented Machine Learning Scientist to join our Global Machine Learning team, driving the development of AI-driven solutions that will shape the future of PayPal. You design and develop ML solutions including personalization, recommendation, and ranking of deals across our Brands and surfaces.

Your goal is to optimize the customer journey through personalized, thrilling experiences. As an ML engineer, you will work on large-scale ranking and recommendation systems for

Qualifications

• Advanced degree in quantitative science or engineering with hands-on experience.
• Experience designing AI/ML models for ranking and recommendation systems.
• Ability to write scalable production code in Python/Java/Scala and build data pipelines.

Responsibilities

• Create AI/ML solutions focusing on personalization, ranking, and recommendations.
• Write scalable production-grade code and deploy models on company infrastructure.
• Collaborate with cross-functional teams to define KPIs and track performance.
• Conduct experiments to measure KPIs and derive actionable insights.

Skills

Ranking models

Recommendation systems

Python

Java

Scala

TensorFlow

PyTorch

Data pipelines

Education

MS/PhD in quantitative science or engineering

Tools

Hive

SQL

BigQuery

Spark

TensorFlow

PyTorch

Job description

PayPal is seeking a talented Machine Learning Scientist to join our Global Machine Learning team, driving the development of AI-driven solutions that will shape the future of PayPal. You design and develop ML solutions including personalization, recommendation, and ranking of deals across our Brands and surfaces.

Your goal is to optimize the customer journey through personalized, thrilling experiences. As an ML engineer, you will work on large-scale ranking and recommendation systems for