ML Engineer: Channel & Guide Personalization for Streaming

Paramount Pictures; New York (NY); On-site; USD 124,000 - 186,000; Full time

Benefits offered by this job

Medical coverage

Dental coverage

Vision coverage

401(k) plan

Life insurance

Disability benefits

Tuition assistance

Paid time off

Job summary

Paramount Pictures seeks a Machine Learning Engineer to join the PlutoTV pod, focusing on channel ranking, guide personalization, and Lean-back optimization.

You will build models on FAST systems, leverage GCP, and use Qdrant for content similarity to enhance the Free Ad-supported Streaming TV experience for a global audience.

Ideal candidates have 3+ years in MLE, Python/SQL, and experience with TensorFlow/PyTorch; knowledge of FAST or broadcast TV data structures is a plus.

Qualifications

• 3+ years in MLE; proficiency in Python/SQL; experience with TensorFlow/PyTorch and GCP.
• Preferred: Knowledge with FAST or Broadcast TV data structures; experience with vector databases like Qdrant.

Responsibilities

• Feature Development: design and deploy ML components for channel ranking and guide personalization.
• FAST Metadata Integration: work with linear scheduling data to account for Current vs Next program relevance.
• Vector-Based Similarity: use Qdrant to group similar channels/content for More Like This in the guide.
• Model Refinement: train and iterate on models focusing on session-start and watch-time metrics.
• Independent Execution: own delivery of tasks within PlutoTV roadmap from data exploration to production deployment.

Skills

Python

SQL

TensorFlow

PyTorch

GCP

ML engineering

Tools

Qdrant

Job description

Paramount Pictures seeks a Machine Learning Engineer to join the PlutoTV pod, focusing on channel ranking, guide personalization, and Lean-back optimization.

You will build models on FAST systems, leverage GCP, and use Qdrant for content similarity to enhance the Free Ad-supported Streaming TV experience for a global audience.

Ideal candidates have 3+ years in MLE, Python/SQL, and experience with TensorFlow/PyTorch; knowledge of FAST or broadcast TV data structures is a plus.