Senior Machine Learning Engineer
CA San Francisco 153 Kearny Street, United States
USD 159K-295K Senior-level Full Time
Tasks
- Apply causal inference for uplift measurement
- Build model dashboards and alerting
- Build scalable inference and feature pipelines on Databricks
- Design ML cost discipline using FinOps
- Design feature engineering training and serving pipelines
- Design offline and online experiments
- Develop and optimize machine learning models
- Enforce training serving consistency and leakage prevention
- Establish feature store strategy and feature contracts
- Implement MLOps for monitoring retraining and versioning
- Lead end to end production ML system development
- Mentor engineers through code reviews and design discussions
- Own flagship ML products
- Prototype agentic ML workflows
- Translate business priorities into executable ML roadmaps
- Use AI-assisted development tools
Perks/Benefits
- N/A
Skills/Tech-stack
AUC-ROC | AWS | AWS SageMaker | Calibration | Causal Inference | Clean room | Cox Proportional Hazards | Data clean room | Databricks | Deep learning | Delta Lake | Drift Detection | Entity Resolution | Feature Engineering | Feature Store | GitHub Copilot | Gradient boosting | Graph Neural Networks | Isotonic Regression | LLM | Langchain | Langgraph | LightGBM | MLOps | MLflow | Machine Learning | Model Deployment | Model Evaluation | Model Monitoring | Model Training | Model versioning | NDCG | Neural Networks | Neural ranking | Precision-recall | Propensity score | PySpark | Python | RAG | SQL | Snowflake | Snowflake Data Clean Room | Snowflake data | Two Tower Retrieval | Two-Tower | Unity Catalog | Uplift Modeling | XGBoost
Education
Regions
Countries
States
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