Senior Machine Learning Engineer, Content Engineering

Senior Machine Learning Engineer, Content Engineering 45530 New York, NY, US, 10036 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. Overview We are seeking a Senior Machine Learning Engineer to lead the development of our multimodal embedding and retrieval systems that power content discovery across Paramount's video library. In this role, you will own the full lifecycle of multi-modal embedding systems, optimized for text and video understanding, from generation, ingestion and indexing, to retrieval — directly impacting how millions of users discover and engage with short-form clips. You will partner with product leadership, Content and Personalization engineering teams, mentor engineers and serve as a senior technical voice shaping how the platform "sees" and retrieves video clip content at scale. The ideal candidate will be: A hands-on systems builder who takes full ownership of pipelines that enable storing, indexing, and querying high-dimensional vector embeddings Skilled at designing hybrid retrieval systems that combine vector similarity, lexical search, and reranking Invested in multimodal video understanding as the foundation for meaningful content representations Skilled at translating embedding system tradeoffs — latency, recall, cost — into product-relevant context that drives cross-functional decisions Committed to mentoring and knowledge sharing with engineering resources Effective at operating in a dynamic environment and comfortable taking ownership of project outcomes end-to-end Responsibilities Video Understanding & Multimodal Embedding Design and build embedding pipelines for video content metadata and clip-level representation Design collection and vector schemas to shape data structure, indexing behavior, and retrieval performance under scale and modality complexity Lead the transition from traditional feature engineering to a vector-centric "context-first" architecture, through compositional queries and by designing high-dimensional hyper-vector representations that unify visual, textual, and behavioral signals Design offline/online evaluation frameworks (e.g., nDCG, MRR, View email address on click.appcast.io) specifically for multimodal alignment, ensuring content embeddings match search intent Vector Search & Retrieval Infrastructure Build hybrid retrieval systems that combine vector similarity search with lexical search and reranking layers to deliver fast, accurate, and scalable performance at production scale Engineer the retrieval layer to capture nuanced user-content relationships that model training alone cannot surface, combining multimodal embeddings to improve recommendation depth at scale Implement query-time optimizations including caching, filtering, and index sharding strategies Tune vector quantization strategies (PQ, SQ, Binary Quantization) to reduce memory footprint and improve search throughput without compromising retrieval precision Own performance SLAs and monitor retrieval systems for latency, throughput, recall, and cost efficiency Production Systems, Pipeline Engineering & APIs Build and maintain scalable batch and streaming pipelines, with logging, metrics, and alerting to surface anomalies and maintain observability Process content at scale using distributed frameworks such as Spark or Ray Architect and build scalable integration layers on top of vector databases, exposing robust APIs and services for similarity search, hybrid retrieval, and metadata filtering Own model versioning and embedding migration strategies, building compatibility tooling that prevents embedding drift from degrading retrieval quality across model upgrades Collaborate with backend and platform teams to ensure interoperability with upstream data pipelines and integration with downstream personalization and discovery surfaces Cross-Functional Leadership & Collaboration Communicate technical system behavior, tradeoffs, and recommendations clearly to both technical and non-technical stakeholders Mentor direct reports, providing technical guidance in multimodal ML, vector retrieval, and production systems design Take ownership of project outcomes from scoping through delivery in a dynamic environment, proactively identifying and mitigating risks across video processing, metadata, and indexing workflows Basic Qualifications 5–8+ years of experience in machine learning engineering, with a focus on production ML systems Expertise in multimodal ML, including experience with video, image, and/or audio embedding models Deep knowledge of vector embedding generation, storage and retrieval, with preference for hands-on Qdrant experience (FAISS, Pinecone, Pgvector, AlloyDB or similar also considered) Strong Python proficiency; Java is a plus Demonstrated experience building and operating data pipelines at scale, including batch and streaming ingestion workflows Solid understanding of hybrid retrieval systems: vector search, lexical search, and reranking Proven ability to communicate technical concepts clearly and partner effectively with product and engineering teams Track record of mentoring engineers and leading technical decisions in a team setting Additional Qualifications Experience with agentic systems and multi-agent orchestration Knowledge of Diversity & Relevance algorithms such as Maximal Marginal Relevance (MMR) within the re-ranking phase Background in video codecs, FFmpeg, or low-level video processing pipelines Awareness with retrieval-augmented generation (RAG) systems VH1