Machine Learning Engineer

Paramount•Burbank, CA

20d•$130,200 - $195,300

About The Position

Paramount is building the next generation of personalization and discovery across our global streaming platforms, and we are looking for a Machine Learning Engineer to contribute to high-impact initiatives across the Paramount+ and Pluto TV sign-up/registration flow. In this role, you will help shape how millions of viewers discover films, series, live sports, and news on Paramount+, Pluto TV, and our future streaming products. You will work closely with senior engineers and applied scientists to build and deploy production systems that improve engagement, retention, and viewer satisfaction. This is a highly collaborative technical role where you will contribute to innovation at the intersection of ML modeling, retrieval and ranking systems, embeddings, experimentation, and real-time user understanding.

Requirements

3–7+ years of experience in machine learning engineering, applied science, recommender systems, or large-scale search/ranking systems.
Experience building and deploying ML systems in production environments.
Solid understanding of modeling techniques such as representation learning, embeddings, and basic recommendation or ranking approaches.
Familiarity with experimentation methodology, A/B testing, and metric evaluation.
Ability to work collaboratively in cross-functional teams and contribute to shared technical goals.
Proficiency with modern ML tooling (e.g., PyTorch, TensorFlow), data processing frameworks (e.g., Spark, Beam, BigQuery), and production ML workflows.

Responsibilities

Contribute to the design and development of personalization in the Paramount+ sign-up flow and Pluto TV registration experiences.
Build and maintain end-to-end machine learning pipelines, including data processing, feature engineering, model training, deployment, and monitoring.
Partner with product, design, content, platform engineering, and data science teams to deliver measurable user outcomes.
Support the development of semantic search and browse experiences using embeddings, query understanding, and domain-specific model architectures.
Apply experimentation best practices, including A/B testing and offline evaluation, to inform model and feature improvements.
Collaborate with and learn from senior engineers and scientists, contributing to a strong and growing Applied ML culture.