Lead Machine Learning Engineer
102557-CA-333 Market, San Francisco, United States
R
USD 224K Senior-level Full Time
Tasks
- Build scalable secure data pipelines
- Create orchestration workflows with reusable DAGs
- Design machine learning pipelines
- Develop transformation libraries
- Document patterns runbooks and best practices
- Enforce data quality lineage and observability
- Implement feature engineering and feature store
- Model predictive training validation and monitoring
- Operationalize reusable data and model pipelines
- Partner to deliver new model use cases
Perks/Benefits
- 401k plan
- Commuter benefits
- Disability benefits
- Discounts and savings
- Hybrid work schedule
- Life insurance
- Paid time off
- Parental leave
- Tuition reimbursement
Skills/Tech-stack
AWS SageMaker | Amazon Bedrock | Anaconda | Apache Beam | Auto Scaling | BigQuery | Bigtable | CI/CD | Canonical Data | Canonical Data Model | Classification | Cloud Composer | Cloud Storage | Cloud Workflows | Cloud platform | Clustering | Data Lineage | Data Model | Data Modeling | Data Quality | Data Vault | Data Vault Modeling | Dataflow | Dataproc | Dimensional Modeling | Docker | Feature Engineering | Feature Store | Forecasting | Git | Google Cloud | Google Cloud Platform | Hugging Face | Jupyter | Knowledge graphs | Liquibase | Logging | MLflow | Machine Learning | Masking | Monitoring | Observability | Partitioning | Prompt engineering | PubSub | PyTorch | Python | RAG | Regression | SQL | Scikit-learn | Semantic layer | TensorFlow | Test automation | Tokenization | Vault modeling | Vector Databases | Vertex AI | XGBoost
Education
N/A
Regions
Countries
States
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