Senior ML Engineer - Multimodal Embeddings & Retrieval

Paramount; New York (NY); On-site; USD 139,000 - 209,000; Full time

Benefits offered by this job

Benefits package

Paid time off

On-site & virtual engagement events

Life at Paramount

Job summary

Paramount Streaming, a division within Paramount Global, seeks a Senior Machine Learning Engineer to own multimodal embedding and retrieval systems powering video discovery. You will design pipelines, hybrids of vector and lexical search, and scalable APIs for large-scale content retrieval.

You will mentor engineers, drive end-to-end delivery, and optimize latency, recall, and cost while collaborating with product and personalization teams to improve user engagement.

Qualifications

• 5–8 years of experience in machine learning engineering with production ML systems.
• Expertise in multimodal ML including video, image, and/or audio embeddings.
• Deep knowledge of vector embedding generation, storage and retrieval; hands-on Qdrant preferred.
• Strong Python; Java is a plus.
• Experience building and operating large-scale data pipelines.

Responsibilities

• Design and build embedding pipelines for video content metadata and clip-level representations.
• Lead the transition to a vector-centric architecture with high-dimensional representations.
• Build hybrid retrieval systems and optimize latency, recall, and cost.
• Own model versioning and embedding migration strategies across upgrades.
• Collaborate with backend teams to expose robust APIs for similarity search and filtering.
• Mentor engineers and communicate tradeoffs to stakeholders.

Skills

Multimodal ML

Video embeddings

Python

Data pipelines

Vector search

Model versioning

Mentoring

Tools

Qdrant

FAISS

Pinecone

Pgvector

AlloyDB

Spark

Ray

Job description

Paramount Streaming, a division within Paramount Global, seeks a Senior Machine Learning Engineer to own multimodal embedding and retrieval systems powering video discovery. You will design pipelines, hybrids of vector and lexical search, and scalable APIs for large-scale content retrieval.

You will mentor engineers, drive end-to-end delivery, and optimize latency, recall, and cost while collaborating with product and personalization teams to improve user engagement.