Staff / Principal Applied AI Researcher (Agentic Search)
Tasks
- Build multi stage retrieval query rewriting and result refinement
- Define evaluation frameworks benchmarks and metrics
- Design retrieval ranking and reranking architectures
- Develop LLM grounding with retrieval from web data
- Drive applied research for agentic search systems
- Enable iterative plan query refine evaluate and reason workflows
- Lead experimentation with embeddings hybrid search and reranking
- Mentor engineers and researchers
- Optimize relevance latency reliability and cost trade offs
- Own research problems through implementation evaluation and deployment
- Translate research into production systems
Perks/Benefits
- Career development and continuous learning opportunities
- Collaborative work environment
- Flexible working environment
- International and diverse working environment
- Meaningful technical ownership
Skills/Tech-stack
Autonomous Agents | Benchmarking | C++ | Deep learning | Embeddings | Go | Hybrid search | Information Retrieval | LLM grounding | Language Models | Large Language Models | Machine Learning | Python | Query Understanding | Query rewriting | RAG evaluation | Recommendation Systems | Reranking | Retrieval-Augmented Generation | Search & Ranking | Tool use | Transformers
Education
N/A
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