Staff ML Scientist – Personalization (Remote)

Apply now!; New York (NY); Remote; USD 210,000 - 250,000; Full time

Job summary

Penguin Random House LLC is seeking a Staff Machine Learning Scientist to lead personalization and recommender systems for our websites, email programs, and online marketing. You will own the end-to-end process from model development to deployment and monitoring, partnering with business stakeholders and platform engineers.

The role requires deep expertise in ML and production-grade systems, with a focus on scalable, low-latency personalized experiences that improve discovery and engagement for

Qualifications

• PhD in Computer Science, Machine Learning, Engineering, Operations Research, Statistics, or a related quantitative field, OR Master’s with 8+ years of applied ML experience.
• Deep expertise in recommender systems, personalization, ranking/retrieval, or computational advertising.

Responsibilities

• Define and drive the technical roadmap for personalization and recommender systems.
• Propose and deliver R&D that shapes roadmaps and multiple projects.
• Design and lead development of software used by multiple teams.
• Ensure multi-service personalization products meet SLAs.
• Establish experimentation best practices and metrics.

Skills

Recommender systems

Personalization

Python

ML frameworks

SQL

Cloud infrastructure

Roadmap leadership

Executive communication

MLOps

Education

PhD in Computer Science or related field / Master’s with 8+ years ML

Tools

NVTabular

Merlin

Triton

Docker

Kubernetes

Databricks

MLflow

Job description

Penguin Random House LLC is seeking a Staff Machine Learning Scientist to lead personalization and recommender systems for our websites, email programs, and online marketing. You will own the end-to-end process from model development to deployment and monitoring, partnering with business stakeholders and platform engineers.

The role requires deep expertise in ML and production-grade systems, with a focus on scalable, low-latency personalized experiences that improve discovery and engagement for