Senior Machine Learning Engineer, Content Engineering

Paramount; New York (NY); On-site; USD 130,000 - 160,000; Full time

Benefits offered by this job

Bonus eligibility

Diversity-focused workplace

Job summary

Paramount is seeking a Senior Machine Learning Engineer in New York to lead the development of multimodal embedding and retrieval systems. You will influence content discovery across the video library, mentor engineers, and enhance video understanding through innovative solutions. The ideal candidate has extensive experience in production ML systems, particularly in handling video and audio embeddings, alongside strong Python skills. This role is bonus eligible and part of a dynamic team.

Qualifications

• 5–8+ years of experience in machine learning engineering focused on production ML systems.
• Expertise in multimodal ML, including experience with video, image, and/or audio embedding models.
• Strong Python proficiency; Java is a plus.

Responsibilities

• Lead development of multimodal embedding systems for video content.
• Design embedding pipelines and implement query-time optimizations.
• Build and maintain scalable batch and streaming pipelines.

Skills

Production ML systems

Multimodal ML expertise

Python proficiency

Data pipelines at scale

Hybrid retrieval systems

Mentoring engineers

Tools

Qdrant

FAISS

Pinecone

Pgvector

AlloyDB

Job description

Overview

We are seeking a Senior Machine Learning Engineer to lead the development of multimodal embedding and retrieval systems that power content discovery across Paramount's video library. 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.

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 high-dimensional hyper-vector representations that unify visual, textual, and behavioral signals
  • Design offline/online evaluation frameworks (e.g., nDCG, MRR, Recall@K) 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 for production-scale performance
  • Engineer the retrieval layer to capture nuanced user-content relationships that model training 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 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 engineers, 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

Qualifications

• Basic
  • 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
  • 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

Paramount is an equal opportunity employer (EOE) including disability/vet. If you are a qualified individual with a disability or a disabled veteran, you may request a reasonable accommodation by calling 212.846.5500 or by emailing [e-mail]. This position is bonus eligible.