ML Engineer
Tasks
- Build golden datasets
- Build vector embeddings ecosystem
- Create ground truth pipelines
- Deploy ML systems in production
- Develop retrieval classification similarity patterns
- Implement PII scrubbing pipelines
- Implement model as a judge
- Improve MLOps with versioning observability drift detection
- Measure inter annotator agreement
- Monitor model performance
- Own model evaluation stack
- Run regression tests for releases
- Support annotation workflows with data science
- Translate product problems into measurable AI features
Perks/Benefits
- Disability insurance
- Life insurance
- Meal stipends
- Medical/Dental/Vision insurance
- Paid parental leave
- Vacation
- Wellness allowance
- Work from home stipend
Skills/Tech-stack
AWS SageMaker | Classification | Drift Detection | Embeddings | Golden datasets | Google Vertex | Google Vertex AI | Inter-annotator agreement | LLM | Label Studio | Language Models | Language Processing | Large Language Models | MLOps | Machine Learning | Model Evaluation | Model Monitoring | Model versioning | NLP | Natural Language | Natural Language Processing | Observability | PII scrubbing | Precision-recall | Python | Ranking | Regression testing | Retrieval | Scale AI | Similarity Search | Vector Databases | Vertex AI
Education
N/A
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