Staff / Principal Applied AI Researcher (Agentic Search)
Tasks
- Build agent native retrieval optimized for machine consumption
- Create evaluation frameworks benchmarks and metrics
- Design multi stage retrieval architectures
- Develop LLM grounded retrieval and reasoning systems
- Develop retrieval and ranking for multi step agent workflows
- Drive applied research direction for agentic search
- Implement iterative plan query refine evaluate workflows
- Lead experimentation with embeddings hybrid search reranking
- Optimize relevance latency reliability and cost trade offs
- Translate research prototypes into production systems
Perks/Benefits
- Career development
- Continuous learning opportunities
- Flexible working environment
- International and diverse work environment
- Meaningful impact in production
- Technical ownership
Skills/Tech-stack
AI Agents | Benchmarks | C++ | Deep learning | Embeddings | Evaluation Frameworks | Go | Hybrid search | Information Retrieval | Language Models | Large Language Models | Machine Learning | Python | Recommendation Systems | Reranking | Retrieval-Augmented Generation | Search & Ranking | Tool use | Transformers
Education
N/A
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