> Markdown version of [/jobs/ext/1816568-principal-machine-learning-engineer](https://www.wearedevelopers.com/jobs/ext/1816568-principal-machine-learning-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal Machine Learning Engineer - **Company:** Paramount Global - **Location:** New York, NY, United States - **Experience:** Expert - **Salary:** $233,600.0 - **Contract:** Permanent contract - **Skills:** A/B Testing, BigQuery, Content Analysis, Machine Learning, Recommender Systems, Tensorflow, Azure Machine Learning, Data Streaming, Google Cloud, Feature Engineering, Pytorch - **Published:** July 28, 2026 - **Apply:** https://www.juju.com/job/00000000gk6twt ## About the Role + 8+ years of experience in MLE, applied science, or large-scale recommender/ranking systems, with a track record of setting technical direction. + Proficient in representation learning. Experience with multi-modal embeddings. Knowledge of contextual bandits and session modeling. + Robust fluency in experimentation methodology, A/B testing, causal reasoning, and metric design. + Proficiency in GCP, TensorFlow, and PyTorch. + Demonstrated ability to influence technical strategy across multiple teams. Bonus Skills (Nice-to-Haves) + Experience in high-traffic, real-time streaming or consumer apps. + Published work or recognized contributions in ranking, recommender systems, or applied ML. ## Description We are looking for a Principal Machine Learning Engineer to set the technical direction for personalization and discovery across our global streaming platforms. Your mission is to define the long-term architecture and modeling strategy. This strategy will help millions of viewers find films, series, live sports, and news. It will include Paramount+, Pluto TV, and future streaming products. You will focus on recommendations, ranking, retrieval, and real-time user awareness. This is a Principal role, meaning you are a senior-most technical authority in the org. You set multi-quarter technical strategy, drive cross-team alignment, and are accountable for the scientific rigor of how we model long-term user satisfaction. You will work in a GCP-based environment. You will use TensorFlow and PyTorch. The focus will be on modern personalization techniques. These techniques include representation learning. They also include multi-modal embeddings, contextual bandits, and session modeling. Why This Role Matters + Setting the Architecture: You define the multi-stage personalization architecture. This includes retrieval, ranking, re-ranking, and exploration. Your work decides what content each user sees. It also influences how users interact, discover new content, and stay involved over time. + Beyond the Click: You establish how we frame and optimize reward signals, ensuring personalization drives durable retention rather than short-term interaction traps. + Org-Level Quality Bar: You raise the technical bar across the Applied ML organization, anticipate systemic risks (data, modeling, feedback loops), and influence the broader personalization roadmap. Responsibilities + Set Technical Strategy: Own the multi-quarter technical roadmap for personalization, covering candidate generation, ranking, and exploration. + Architect end-to-end systems. Design multi-stage personalization systems. These systems include retrieval, deep ranking, contextual embeddings, and bandit-based exploration. You will manage the entire process, from feature engineering to training, serving, and monitoring. + Advance modeling in production. Drive advanced techniques. Use representation learning, multi-task learning, multi-modal embeddings, and session modeling. These strategies will help improve user satisfaction. + Cross-Pod Influence: Partner with Core Science, Content Understanding, ML Platform, and Product to align personalization with broader strategy. + Operate at Scale: Ensure personalization pipelines are high-throughput, reliable, and observable in GCP using TensorFlow/PyTorch and big-data tooling (Beam, BigQuery). + Raise the quality of experimentation. Establish practices that ensure high integrity in experiments. Improve the correlation between offline and online results. Guide feature rollouts with solid scientific methods. + Mentorship & Talent: Mentor engineers, set technical standards across the org, and grow the next generation of senior ML talent. + Mitigate Systemic Risk: Identify and address feedback loops, exposure biases, and filter-bubble dynamics in how content is surfaced. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [How We Built a Machine Learning-Based Recommendation System (And Survived to Tell the Tale)](https://www.wearedevelopers.com/videos/752-how-we-built-a-machine-learning-based-recommendation-system-and-survived-to-tell-the-tale) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [Overview of Machine Learning in Python](https://www.wearedevelopers.com/videos/840-overview-of-machine-learning-in-python) - [Getting Started with Machine Learning](https://www.wearedevelopers.com/videos/260-getting-started-with-machine-learning) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [The Best Large Language Models on The Market](https://www.wearedevelopers.com/magazine/319-the-best-large-language-models-on-the-market)