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.

• 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 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 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
• 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

• Communicate technical system behaviour, 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

• 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…