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

Skills & Qualifications Required Skills

Backend: Experience with API development and RESTful services, ideally with proficiency in Django/Python.
Vector Databases: Experience with vector databases, especially FAISS or Milvus, and how they integrate with machine learning systems.
LLM Knowledge: Understanding and hands-on experience working with Large Language Models (LLMs) in applications.
Search Stacks & Agents: Previous experience building search stacks, agents, or intelligent information retrieval systems.
RAG Pipelines: Knowledge of building and enhancing Retrieval Augmented Generation (RAG) pipelines

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