Staff / Principal Applied AI Researcher (Agentic Search)
Tasks
- Build agent native retrieval systems for machine consumption
- Define evaluation frameworks benchmarks and metrics for agentic systems
- Design multi stage retrieval architectures
- Develop methods for real time retrieval evaluation and reasoning over web data
- Develop retrieval and ranking for multi step agent workflows
- Drive applied research and technical direction across agentic search systems
- Implement LLM iterative planning query refinement evaluation and reasoning workflows
- Lead experimentation with embeddings hybrid search and reranking
- Mentor engineers and researchers
- Optimize relevance latency reliability and infrastructure cost trade offs at scale
- Own ambiguous problems through implementation evaluation and deployment
- Translate research into production systems with engineering teams
Perks/Benefits
- Career development
- Continuous learning opportunities
- Flexible working environment
- International and diverse working environment
- Technical ownership
Skills/Tech-stack
Benchmarks | C++ | Deep learning | Embeddings | Evaluation | Go | Hybrid search | Information Retrieval | Language Models | Large Language Models | Machine Learning | Metrics | Python | Query Understanding | Query rewriting | Ranking | Recommendation Systems | Reranking | Retrieval-Augmented Generation | Search | Transformers
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
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