ML Engineer
Tasks
- Build and maintain vector embeddings ecosystem
- Deploy and monitor machine learning systems in production
- Implement retrieval classification and similarity patterns
- Improve MLOps foundations for versioning observability and drift detection
- Own model evaluation stack and gate releases
- Partner with data science on annotation workflows and ground truth pipelines
- Translate product problems into measurable AI features
Perks/Benefits
- Business travel insurance
- Cell phone stipend
- Flexible work model
- Home office setup support
- Internet stipend
- Learning platform
- Life insurance
- Meal stipends
- Medical insurance
- Mental wellness support
- Paid parental leave
- Professional development support
- Stock options
- Tuition reimbursement
- Vacation time
- Wellness allowance
Skills/Tech-stack
AWS | Annotation Workflows | Data Processing | Drift Detection | Embeddings | Entity recognition | GCP | Golden datasets | Inter-annotator agreement | LLM | MLOps | Machine Learning | Model Evaluation | Model as a judge | Model versioning | NLP | Named Entity Recognition | Observability | PII scrubbing | Precision-recall | Python | Regression testing | Retrieval systems | Vector Databases
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
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