About this role

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

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

Machine Learning Engineer, Entry

Key skills