Principal Machine Learning Engineer, Ads Personalization

Principal Machine Learning Engineer, Ads Personalization

Technology New York Full-Time Fully Remote #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.

Role Overview

We are seeking a Senior Lead/Principal Machine Learning Engineer to lead the Ads Pod, the team in charge of architecting the expanded intelligent core of precision, our proprietary in-house ad system. Your mission is to reinvent how monetization works in the streaming era by building the algorithms that power ad selection, podding, and result optimization across PlutoTV and Paramount+. This is a high-stakes technical leadership role. You will lead a group of engineers and scientists to advance our platforms beyond standard ad insertion into a world of dynamic, revenue-aware personalization. You will solve the unique task of balancing the "lean-back" linear experience of PlutoTV with the high-intent VOD environment of Paramount+, ensuring that every ad served adds value to the viewer while maximizing return for the business. A critical part of this role is working with Signal Intelligence (SigInt). You will build optimization models with multiple goals. These models will balance long-term watch time and short-term ad revenue. This helps create a sustainable and enjoyable experience for millions of viewers. The Ads Pod sits at the intersection of user experience and business sustainability. In this role, you will directly shape:

• The Precision+ Engine: Owning the ML strategy for our in-house ad-tech stack, shifting from third-party dependencies to a bespoke, high-performance internal system.
• Revenue-Aware Personalization: Solving the ultimate trade-off in streaming: maximizing long-term user commitment (watch time) while optimizing short-term ad-fill and output.
• Unified Ad Intelligence: Building a cross-platform system that understands viewer behavior on both FAST (PlutoTV) and Premium VOD (Paramount+) to deliver the most relevant ad experience in the industry.

Key Responsibilities

Lead the Ad Pod: Drive the technical roadmap for all Pluto-specific ML use cases, acting as the bridge between AMLG's core infrastructure and Pluto's product goals.

Personalized Channel Discovery: Architect retrieval and ranking systems to recommend the right linear channels to users in real-time.

Dynamic Scheduling & Creation: Develop models that optimize channel scheduling and inform the creation of "Pop-up" or algorithmic channels based on viewer trends.

Multi-Objective Optimization: Design loss functions and reward systems that weigh user commitment (watch time) against business health (ad impressions and revenue).

EPG Personalization: Lead the vision for personalizing the Electronic Programming Guide, ensuring the most relevant content is surfaced immediately upon app launch.

Cross-Functional Leadership: Partner with RevOps and Product to translate business constraints (e.g., ad delivery guarantees) into technical ML requirements.

Basic Qualifications

6-8+ years of experience in machine learning engineering, with a significant focus on Ad-Tech, Auction Dynamics, or Recommender Systems.

Optimization Expertise: Deep mastery of Multi-Objective Optimization and constrained optimization (balancing competing KPIs like revenue vs. UX).

Real-Time Systems: Proven experience building and deploying ML models in high-throughput, ultra-low latency environments (under 50ms).

Technical Stack: Skilled in Python, PyTorch/TensorFlow, and BigQuery; experience with high-scale serving layers.

Leadership (IC5/IC6): Demonstrated ability to own a major technical domain and drive strategy across multiple organizations.

Additional Qualifications

Experience building or scaling in-house ad systems or DSP/SSP components.

Knowledge of Reinforcement Learning (RL) for sequential ad-podding and frequency capping.

Knowledge of Causal Inference to measure the incremental boost ad-personalization on long-term subscriber churn.

Experience in both FAST (Linear) and VOD advertising ecosystems.