ML Engineer
Tasks
- Build evaluation stack for ML models
- Build vector embeddings ecosystem
- Deploy ML systems in production
- Detect data drift
- Detect model drift
- Develop classification and ranking workflows
- Develop retrieval and similarity patterns
- Implement ground truth pipelines
- Implement model as a judge frameworks
- Improve MLOps foundations
- Maintain golden datasets
- Measure inter annotator agreement
- Monitor ML systems in production
- Partner on annotation workflows
- Run regression tests gating releases
- Scrub PII in data pipelines
- Set up versioning and observability
- Translate product problems into AI features
Perks/Benefits
- 15 days vacation
- 401k plan
- Floating holidays
- Learning platform
- Life and disability insurance
- Meal stipends
- Medical, dental, and vision insurance
- Paid parental leave
- Set holidays
- Wellness allowance
- Work from home stipend
Skills/Tech-stack
AWS | Classification | Data Privacy | Data Versioning | Drift Detection | Embeddings | Entity recognition | GCP | Golden datasets | Information Retrieval | Inter-annotator agreement | LLMs | MLOps | Model Deployment | Model Evaluation | Model Monitoring | NLP | Named Entity Recognition | Observability | PII scrubbing | Precision-recall | Python | Ranking | Similarity Search | Vector Databases
Education
N/A
Regions
Countries
States
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