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Paramount

Principal Machine Learning Engineer

Paramount

Principal Machine Learning Engineer driving technical strategy and architecture for short-form personalization at Paramount. Mentoring engineers and optimizing ranking systems for long-term user satisfaction.

Posted 5/16/2026full-timeRemote • New York • 🇺🇸 United StatesLead💰 $233,000 - $350,000 per yearWebsite

Tech Stack

Tools & technologies
Google Cloud PlatformPyTorchTensorflow

About the role

Key responsibilities & impact
  • Own the multi-quarter technical roadmap for short-form ranking, candidate generation, and Post-training RL.
  • Design the multi-stage ranking architecture spanning retrieval, ranking, re-ranking, and RL-based policy optimization.
  • Drive the use of post-training reinforcement learning techniques, including reward modeling, off-policy evaluation, and policy alignment to improve user satisfaction over long periods.
  • Partner with Content Understanding, ML Platform, Core Science, and Product to align short-form personalization with broader Discovery strategy.
  • Ensure ranking pipelines are high-throughput, reliable, and observable in GCP using TensorFlow/PyTorch.
  • Mentor IC1–IC3 engineers, set technical standards across the pod, and grow the next generation of senior ML talent.
  • Identify and resolve feedback loops, exposure biases, and filter-bubble dynamics in how short-form content is surfaced.

Requirements

What you’ll need
  • 8+ years of experience in MLE with a track record of setting technical direction for large-scale ranking or recommender systems
  • deep expertise in Reinforcement Learning, particularly Post-training RL and long-horizon reward modeling
  • proficiency in GCP, TensorFlow, and PyTorch; demonstrated ability to influence technical strategy across multiple team

Benefits

Comp & perks
  • medical
  • dental
  • vision
  • 401(k) plan
  • life insurance coverage
  • disability benefits
  • tuition assistance program
  • PTO

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools
Reinforcement LearningPost-training RLreward modelingoff-policy evaluationpolicy alignmentranking architectureranking pipelineshigh-throughput systemsre-rankingcandidate generation
Soft Skills
mentoringtechnical leadershipcollaborationinfluencing strategyproblem-solvingsetting technical standards