Machine Learning Engineer - Presentation and Visual Optimization

This is a Full Time Job

#WeAreParamount on a mission to unleash the power of content… you in?
We’ve got the brands, we’ve got the stars, we’ve got the power to achieve our mission to entertain the planet – now all we’re missing is… YOU! Becoming a part of Paramount means joining a team of passionate people who not only recognize the power of content but also enjoy a touch of fun and uniqueness. Together, we co-create moments that matter – both for our audiences and our employees – and aim to leave a positive mark on culture.

Machine Learning Engineer, Presentation and Visual Optimization(45540)

Overview:

We are seeking a Machine Learning Engineer to join our Presentation pod. While other teams within AMLG focus on the ''recommender(what to show), our pod owns the ''visual gateway.'' Your mission is to implement and scale the ML systems that optimize how content is displayed to capture user attention. You will own the development of specific features and components within our artwork selection, marquee personalization, and carousel design pipelines. In this role, you will be expected to deliver autonomously on defined technical problems, ranging from bandit-based reward signals to computer vision feature extraction. You will work in a high-velocity environment where visual and attention optimization are the primary goals, requiring you to balance technical quality with delivery pace.

Why This Role Matters as an individual contributor in the presentation pod, you are the engine of execution. In this role, you will:

Own the Visual Gateway: Deliver the features that identify the first thing millions of users see when they open our apps.

Drive Systematic Quality: Improve the reliability and velocity of our experimentation systems, ensuring our visual tests are statistically sound and performant.

Scale Discovery: Build the components that allow us to personalize the ''look and feel'' of the platform, not just the content list.

Key Responsibilities:

Feature & Component Ownership: Design and implement specific solutions for Multi-Armed Bandit (MAB) systems and visual feature pipelines.

Self-governing Delivery: Own the end-to-end implementation of defined tasks, from data ingestion to production deployment, with moderate autonomy. System Optimization: Proactively identify and fix bottlenecks in team systems to improve quality, reliability, or engineering velocity. Collaborative Quality: Participate actively in design and code reviews, providing constructive feedback and ensuring high technical standards within your scope. Data-Driven Execution: Set up and monitor online experiments (A/B tests and bandit rollouts) to measure the impact of presentation features on user interaction

Basic Qualifications
3 years of experience in machine learning engineering or backend software engineering.

Proven Delivery: Experience owning and delivering technical features or components autonomously.

Technical Stack: Proficiency in Python and experience with ML frameworks like PyTorch or TensorFlow.

Data Foundations: Strong skills in SQL and experience with distributed data processing (e.g., Spark or Databricks).

Engineering Rigor: Familiarity with version control, CI/CD, and writing production-grade, maintainable code.

Additional Qualifications
Familiarity with Multi-Armed Bandits or Reinforcement Learning concepts. Background in Computer Vision or image processing.

Experience in a high-scale streaming or e-commerce environment. Experience with Cloud Infrastructure, including AWS, GCP, and Azure.

#LI-RJ1