Senior Machine Learning Engineer, Content Engineering

#We Are Paramount  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.

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

• Design and build embedding pipelines for video content metadata and clip‑level representation
• Design collection and vector schemas to shape data structure, indexing behaviour, 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 behavioural signals
• Design offline/online evaluation frameworks (e.g., nDCG, MRR, Recall@K) specifically for multimodal alignment, ensuring content embeddings match search intent

• 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 optimisations including caching, filtering, and index sharding strategies
• Tune vector quantisation strategies (PQ, SQ, Binary Quantisation) 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

• 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…