Principal Machine Learning Engineer, Content Engineering

Paramount•New York, NY

About The Position

We are seeking a Senior Lead / Principal Machine Learning Engineer to lead our ShortForm & Video Intelligence pod. This team is in charge of a critical mission: the automated transformation of our massive long-form content library into "ShortForm" assets—high-impact clips, "gists," and social-ready highlights. You will lead the development of the models that "watch" our content to identify narrative hooks, emotional climaxes, and key characters to create compelling bite-sized experiences. This is a deeply technical leadership role requiring mastery of multi-modal machine learning. You will oversee the creation of intelligence layers that perform video safety analysis (nudity/profanity detection), spoiler detection, and sophisticated narrative comprehension. Your work ensures that our ShortForm content is not only engaging but also safe, contextually precise, and optimized for discovery across the Paramount ecosystem. The ShortForm pod unlocks the value of our entire catalog. In this role, you will directly shape: The "Gist" Ecosystem: Building the engine that generates short-form previews, allowing users to "snack" on content before committing to a full watch. Platform Safety & Integrity: Owning the "Video Intelligence" filters that protect our brand and users through automated detection of sensitive or restricted content. Narrative AI: Transitioning beyond simple metadata to a world where AI understands story arcs, character importance, and "viral" moments within a film or episode.

Requirements

6-8+ years of experience in machine learning, with a heavy focus on Computer Vision (CV) and Multi-Modal architectures.
Deep Learning Expertise: Mastery of PyTorch or TensorFlow, specifically for video-based tasks (e.g., Video Transformers, 3D CNNs). Video Intelligence: Proven experience building safety/moderation models (Nudity, Profanity, Hate Speech) or content-tagging systems.
Leadership: Experience leading senior technical teams and translating complex "creative" goals into engineering requirements.
Video Tech Stack: Knowledge of video processing frameworks (FFmpeg) and handling various codecs/resolutions at scale.

Nice To Haves

Experience with Generative AI for Video (automated editing or style transfer).
Background in NLP/Speech for sync-ing transcripts with video frames.
Knowledge of Narrative Theory or experience in the media/entertainment industry.
Experience deploying large-scale models in Cloud Environments (AWS/GCP/Azure) with high GPU utilization efficiency.

Responsibilities

Lead the ShortForm Pod: Define the technical roadmap for automated clip generation and video insight, managing a high-performing pod of ML engineers.
Architect Multi-Modal Models: Design and deploy models that synthesize signals across video (pixels), audio (speech/music), and text (transcripts/scripts) to understand content context.
Own Video Intelligence: Develop and scale robust computer vision and audio classifiers for nudity, profanity, and sensitive content detection.
Advance Narrative Understanding: Build models that can identify "hooks," "climax points," and "narrative summaries" to automate the creation of trailers and highlights.
Spoiler & Context Detection: Create intelligence layers that identify plot-critical "spoilers" to ensure ShortForm assets don't ruin the viewing experience for new users.
Scalable Content Pipelines: Partner with Content Engineering to integrate these models into petabyte-scale video processing workflows.