Tasks:
- * Align and evolve enterprise ontologies
- * Assess agent processes and outcomes
- * Build hybrid retrieval and reranking algorithms
- * Deploy and optimize algorithms in production
- * Design query decomposition and context assembly
- * Diagnose production failures from tracing and feedback
- * Evaluate evidence quality and answer reliability
- * Extract enterprise entities and relationships
- * Generate governed learning recommendations
- * Map data assets to business concepts
Perks/Benefits:
- + Benchmark development opportunities
- + End-to-end ownership opportunities
- + Industry standards contributions
- + Open source opportunities
- + Patent opportunities
- + Publication opportunities
- + Real-world research problems
- + Research-to-production development
- + Technical report opportunities
Skills/Tech stack required:
[Active Learning] [Agent evaluation] [Benchmarking] [Causal Inference] [Context engineering] [Deep learning] [Experimental Design] [Graph Learning] [Hybrid retrieval] [Information Retrieval] [Knowledge graphs] [Language Processing] [Machine Learning] [Natural Language] [Natural Language Processing] [Ontology Engineering] [Python] [PyTorch] [Reinforcement Learning] [Representation Learning] [Reranking] [Semantic mapping] [Weak Supervision]
Educational requirements:
[Master's Degree]
Role(s):
[Artificial Intelligence] [Artificial Intelligence Researcher] [Engineer] [Information Retrieval Researcher] [Intelligence Researcher] [Knowledge Graph Researcher] [Learning Engineer] [Machine Learning Engineer] [Natural Language Processing Researcher] [Researcher]