Principal Machine Learning Engineer, Personalization

Paramount

. 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.

Posted 4/22/2026full-timeRemote • New York • 🇺🇸 United StatesLead💰 $234,000 - $260,000 per yearWebsite

Tech Stack

Tools & technologies

BigQueryPythonPyTorchTensorflow

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

6-8+ years of experience in machine learning engineering, with a significant focus on Ad-Tech, Auction Dynamics, or Recommender Systems.
Deep mastery of Multi-Objective Optimization and constrained optimization (balancing competing KPIs like revenue vs. UX).
Proven experience building and deploying ML models in high-throughput, ultra-low latency environments (under 50ms).
Skilled in Python, PyTorch/TensorFlow, and BigQuery; experience with high-scale serving layers.
Demonstrated ability to own a major technical domain and drive strategy across multiple organizations.
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.

Benefits

Comp & perks

medical
dental
vision
401(k) plan
life insurance coverage
disability benefits
tuition assistance program
PTO
bonus eligible

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

machine learning engineeringMulti-Objective Optimizationconstrained optimizationPythonPyTorchTensorFlowBigQueryReinforcement LearningCausal Inferencead-personalization

Soft Skills

cross-functional leadershipstrategic thinkingcommunicationcollaborationproblem-solving