Lead Machine Learning Engineer
San Diego, CA, United States
USD 109K-230K (estimate) Senior-level Full Time
Tasks
- Architect and build systems to train deploy run inference and monitor ML and AI models
- Build batch and streaming data pipelines
- Build data driven APIs microservices and event driven systems
- Champion code quality reusability scalability maintainability and security
- Collaborate with cross functional partners to build ML products
- Design and evaluate machine learning models
- Develop MLOps workflows for deployment monitoring and drift detection
- Implement data quality processes and data governance policies
- Integrate ML and AI systems with production applications
- Load test deployed models to identify performance bottlenecks
- Orchestrate workflows and data pipelines with Airflow
- Own objectives and technical solutions for a workstream
Perks/Benefits
Skills/Tech-stack
AWS | Airflow | Artificial Intelligence | Bias Variance | Bias Variance Tuning | CI/CD | Data Drift | Data Drift Detection | Data Governance | Data Lakes | Data Quality | Data Warehousing | Dimensional Modeling | Dimensionality Reduction | Docker | Drift Detection | Event Driven | Event-driven architecture | Fraud Detection | Generative AI | Git | Huggingface | Keras | Knowledge graphs | Kubernetes | Lakehouses | Language Models | Large Language Models | MLOps | MLflow | Machine Learning | Marketing Science | Microservices | Model Evaluation | Model Monitoring | Model Validation | Pandas | Personalization | PySpark | PyTorch | Python | Recommender Systems | SQL | Scikit-learn | Sensitivity Analysis | TensorFlow | Vector Databases
Education
Regions
Countries
States
Cities
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