Senior ML Engineer - Multimodal Embeddings (Remote)

Paramount Pictures; New York (NY); On-site; USD 139,200 - 208,800; Full time

Benefits offered by this job

Competitive compensation

Generous PTO

On-site events

Virtual events

Job summary

Paramount Pictures seeks a Senior Machine Learning Engineer to lead multimodal embedding and retrieval systems powering content discovery across Paramount's video library. You will own the full lifecycle from generation, ingestion and indexing to retrieval, impacting how millions of users find short-form clips.

You will mentor engineers, design hybrid retrieval systems combining vector similarity and lexical search, and partner with product and personalization teams to shape the platform that

Qualifications

• 5–8+ years of experience in production ML systems
• Expertise in multimodal ML with video/image embeddings
• Hands-on vector embedding generation, storage and retrieval (Qdrant preferred)
• Strong Python; Java a plus
• Experience building scalable data pipelines (batch and streaming)
• Knowledge of hybrid retrieval: vector search, lexical search, and reranking
• Experience mentoring engineers and leading technical decisions

Responsibilities

• Design and build embedding pipelines for video content metadata and clip-level representations
• Lead hybrid retrieval system design with vector and lexical search and reranking
• Own model versioning and embedding migration strategies
• Collaborate with backend and platform teams for integration

Skills

Multimodal ML

Python

Vector retrieval

Pipeline design

Mentoring engineers

Tools

Qdrant

FAISS

Pinecone

Job description

Paramount Pictures seeks a Senior Machine Learning Engineer to lead multimodal embedding and retrieval systems powering content discovery across Paramount's video library. You will own the full lifecycle from generation, ingestion and indexing to retrieval, impacting how millions of users find short-form clips.

You will mentor engineers, design hybrid retrieval systems combining vector similarity and lexical search, and partner with product and personalization teams to shape the platform that