Machine Learning Engineer, Entry

Paramount is on a mission to unleash the power of content, and they are seeking an Entry-level Machine Learning Engineer to join their Applied Machine Learning Group. The role involves building a world-class streaming experience by optimizing user engagement through machine learning techniques.

Responsibilities:

• Independent Delivery: Own the implementation of features for JBI (Jump Back In) and YNW (Your Next Watch)
• Retrieval Optimization: Use Qdrant to find high-relevance candidates for re-entry carousels based on session history and global trends
• Experiment Lifecycle: Use MLFlow to manage, track, and deploy experiments, ensuring a high bar for reproducibility
• Model Training: Develop and serve models in GCP using TensorFlow/PyTorch, incorporating Post-training RL for reward-based optimization
• Collaborative Quality: Participate in design reviews to ensure your components share the same high-reliability standards as the rest of the pod
• Solving Cold Start: Build the logic that makes the app feel personalized even for users we know very little about
• High-Commitment Accuracy: Optimize for the "Play" button—the ultimate signal of user commitment

Requirements:

• 3+ years in MLE
• Experience with GCP and MLFlow
• Proficiency in TensorFlow/PyTorch
• Knowledge with Vector DBs
• Proven ability to implement features that solve the 'Cold Start' problem for new users
• Experience managing, tracking, and deploying experiments to ensure a high bar for reproducibility
• Background in multi-stage ranking or 'Cold Start' problems
• Experience with retrieval optimization and re-entry carousels
• Knowledge with Post-training RL for reward-based optimization