Research Scientist, Search & AI

Measurement

• Constructing measures of search reliability/comprehensiveness and success/effectiveness; comparing ordered sets of search results; relevant notions of similarity and distance to evaluate IR

• Implementing and automating benchmarks using labeled data and expert human feedback, interfacing with our legal research fellows

• Measures of the popularity of a result for a class of queries, to enable reranking by observing users

Optimization/Scaling/Elasticity

• Indexing algorithms and data structures, and infrastructure for billion-scale, multi-engine search

• Pre- and post-processing hacks, eg. query design

• Segmentation, sharding, concurrency and parallelism, and other clever distribution methods—optimizing latency and time and memory complexity

• Low-dimensional/cost-optimized retrieval methods for enterprise settings

Invention

• Identifying, finetuning, and aligning new vector embedding methods

• Reranking and boosting from data augmentation and live user interaction emit

  • Caching mechanisms for high-variance natural language queries