Principal Applied Scientist
Tasks
- Advance multi turn agent experiences
- Build grounding systems for source backed answers
- Define evaluation methods and success metrics
- Develop and improve ranking and reranking models
- Improve end to end search and retrieval quality
- Lead applied science for grounding search retrieval and ranking systems
- Partner with engineering product and research teams to ship improvements
- Tune and optimize models for grounded and agentic behavior
Perks/Benefits
- N/A
Skills/Tech-stack
A/B | A/B Testing | Agentic AI | B testing | Conversational AI | Data Curation | Data Generation | Distributed Training | Error Recovery | Evaluation Design | Fine Tuning | Grounded generation | Human evaluation | Information Retrieval | Language Models | Large Language Models | Learning-to-Rank | Machine Learning | Machine Learning Pipelines | Model Distillation | Multi Turn Conversational AI | Offline evaluation | Online evaluation | Planning | Preference optimization | Production Machine Learning | Production Machine Learning Pipelines | Ranking Models | Reranking | Retrieval-Augmented Generation | Reward Modeling | Supervised Fine Tuning | Synthetic Data Generation | Synthetic data | Tool use
Education
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