Lead Machine Learning Engineer, Entry and Re-engagement
Tasks
- Build next watch and high commitment ranking engine
- Contribute to technical vision for re engagement and onboarding models
- Define success metrics start rate time to play day 1 retention
- Design hybrid retrieval pipelines with feature and vector search
- Implement exploration exploitation strategies for user intent uncertainty
- Integrate retrieval with downstream ranking models
- Lead ML strategy for onboarding and re engagement
- Mentor engineers and lead ML projects from research to production
- Optimize re entry surfaces with temporal decay and cross device signals
- Own production system performance with SLOs
- Run A B testing and online experimentation frameworks
- Solve cold start using minimal metadata and global trends
Perks/Benefits
- 401k plan
- Disability benefits
- Life insurance
- Medical/Dental/Vision
- PTO
- Paid time off
- Tuition assistance
Skills/Tech-stack
A/B | A/B Testing | Airflow | Apache Beam | Apache Spark | Argo Workflows | B testing | Bandit Algorithms | Causal Inference | Distributed Systems | Docker | Experimentation | Information Retrieval | JAX | Kubernetes | Learning-to-Rank | MLOps | MLflow | Machine Learning | NVIDIA Triton | Neural Networks | PyTorch | RNN | Ranking | Ray | Recommendation Systems | Recommender System | Recommender Systems | Reinforcement Learning | Retrieval Pipeline | Search | Sequential Models | Serving frameworks | TensorFlow | Transformers | Vector Search | Weights and Biases
Education
N/A
Regions
Countries
States
Cities
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