Staff / Principal Applied AI Researcher (Agentic Search)
Tasks
- Build agent native retrieval systems
- Create evaluation frameworks and benchmarks for agentic systems
- Design multi stage retrieval architectures
- Develop LLM integrated retrieval and reasoning systems
- Drive applied research and technical direction across retrieval and agentic search systems
- Lead experimentation with retrieval technologies
- Mentor engineers and researchers
- Optimize relevance latency reliability and cost trade offs
- Own ambiguous problems from research through production deployment
- Translate research into production systems
Perks/Benefits
- Career development
- Collaborative environment
- Continuous learning opportunities
- Flexible working environment
- International and diverse work environment
- Technical ownership
Skills/Tech-stack
Autonomous Agents | Benchmarks | C++ | Deep learning | Embeddings | Evaluation metrics | Go | Hybrid search | Information Retrieval | Knowledge 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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