Lead Machine Learning Engineer
102557-CA-333 Market, San Francisco, United States
R
USD 224K Senior-level Full Time
Tasks
- Architect ML and generative AI data architectures
- Build data models and semantic layers in BigQuery
- Design scalable secure data pipelines
- Develop and deliver new model use cases
- Develop data transformation libraries
- Document patterns runbooks and best practices
- Enforce data quality lineage and observability
- Implement feature engineering and feature store
- Implement orchestration workflows and reusable DAGs
- Operationalize predictive machine learning lifecycles
- Optimize cost performance and reliability for GCP workloads
Perks/Benefits
- 401k plan
- Commuter benefits
- Disability benefits
- Hybrid work
- Life insurance
- Paid time off
- Parental leave
- Tuition reimbursement
Skills/Tech-stack
Airflow | Anomaly Detection | Apache Beam | Automated testing | Bias detection | BigQuery | BigQuery Semantic Layer | Bigtable | CI/CD | Canonical Modeling | Classification | Cloud Composer | Cloud Monitoring | Cloud Storage | Cloud Workflows | Cloud logging | Data Lineage | Data Modeling | Data Quality | Data Vault | Dataflow | Dataproc | Dimensional Modeling | Drift Detection | Elasticsearch | Error Reporting | Feature Engineering | Feature Store | Forecasting | Git | Jupyter | Liquibase | MLflow | Machine Learning | MongoDB | Monitoring | Observability | Pub/Sub | PyTorch | Python | Redis | Regression | SQL | Scikit-learn | Semantic layer | Spark | Starburst | Starburst Trino | TensorFlow | Trino | Vertex AI | XGBoost
Education
N/A
Regions
Countries
States
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