Head of Applied Machine Learning
Tasks
- Apply LLMs for semantic understanding and intent detection
- Build recommendation and curation models
- Collaborate with product and engineering teams on ML adoption
- Define evaluation metrics and experimentation plans
- Design testing deploying and monitoring for LLM models
- Develop ranking and personalization systems
- Implement retrieval-augmented generation
- Lead applied machine learning model development
- Mentor and manage applied machine learning practitioners
- Own offline and online experimentation
- Translate business problems into modeling objectives
Perks/Benefits
Skills/Tech-stack
Data Science | Embeddings | Evaluation metrics | Experimentation | Feature Engineering | Fine Tuning | Gradient boosting | Language Models | Large Language Models | Learning-to-Rank | Machine Learning | Model Deployment | Model Monitoring | Neural Networks | Personalization | Prompt engineering | Ranking Systems | Recommendation Systems | Retrieval-Augmented Generation | Testing
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
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