Senior ML Engineer - Audience Enrichment
Tasks
- Build production machine learning pipelines for training evaluating deploying
- Collaborate in technical discussions and code reviews
- Create documentation for machine learning systems feature pipelines and runbooks
- Design feature engineering pipelines using large scale data processing
- Develop and optimize end to end machine learning solutions for audience segmentation
- Implement machine learning best practices with Git automated testing CI CD
- Implement model monitoring for performance and data drift
- Improve model efficiency inference latency and resource utilization
- Productionize research models into scalable production systems
- Translate business requirements into scalable machine learning solutions
- Troubleshoot data pipeline failures model serving and data quality issues
- Tune validate optimize machine learning models for accuracy efficiency scalability
Perks/Benefits
Skills/Tech-stack
A/B | A/B Testing | Apache Beam | Apache Spark | B testing | CI/CD | Cloud Dataflow | Composer | Feature Engineering | Git | Google BigQuery | Google Cloud | Google Cloud Dataflow | Hyperparameter Tuning | Java | MLOps | Machine Learning | Model Evaluation | Model Serving | Model versioning | Monitoring | PyTorch | Python | SQL | Scikit-learn | TensorFlow | Vertex AI | XGBoost
Education
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