Information Retrieval Engineer

Adobe • San Jose, CA

7d • $125,600 - $234,150

About The Position

We are seeking a highly skilled Information Retrieval Engineer to lead the development and optimization of retrieval systems that power context-aware large language models (LLMs). This role focuses on building robust Retrieval-Augmented Generation (RAG) pipelines to ensure AI agents and applications have access to the most relevant, timely, and high-quality information. You'll work at the intersection of data engineering, machine learning, and knowledge management—enabling better reasoning, accuracy, and performance for enterprise-grade AI systems.

Requirements

• 4+ years in data engineering, ML infrastructure, or information retrieval
• Experience building and deploying RAG pipelines or semantic search systems
• Strong Python skills and familiarity with retrieval libraries (e.g., Haystack, LangChain, Elasticsearch, Milvus)
• Proficiency with embedding models, vector similarity search, and document indexing
• Familiarity with cloud platforms and MLOps tooling (e.g., Airflow, dbt, Docker)

Nice To Haves

• Knowledge of graph databases (e.g., Neo4j, TigerGraph) or knowledge graph design
• Experience optimizing retrieval for LLMs (e.g., OpenAI, Anthropic, Mistral)
• Background in IR/NLP, Search Engineering, or Cognitive Computing
• Bachelors or Masters Degree in Computer Science, Information Systems, or a related field

Responsibilities

• RAG System Design: Architect and deploy scalable retrieval pipelines using vector databases (e.g., FAISS, Weaviate, Pinecone, Qdrant)
• Implement semantic search infrastructure and hybrid retrieval systems (semantic + keyword)
• Data Processing & Ingestion: Build ingestion pipelines for both structured and unstructured data sources
• Implement document chunking strategies, embedding generation (e.g., OpenAI, Cohere, HuggingFace), and metadata tagging
• Retrieval Optimization: Fine-tune relevance scoring, reranking algorithms, and query understanding mechanisms
• Develop techniques to improve precision/recall for specific business domains or user tasks
• Knowledge Enhancement: Create and maintain knowledge graphs to support context linking and disambiguation
• Manage data freshness and version control to ensure consistency and reliability of retrieved content
• Reasoning Support: Design and iterate on context window strategies that improve LLM reasoning (e.g., adaptive injection, task-based retrieval)
• Collaborate with prompt engineers and model developers to align retrieval outputs with downstream model behavior
• Performance Monitoring: Track key retrieval metrics such as accuracy, latency, and fallback rate
• Implement caching, prefetching, and deduplication strategies to optimize system responsiveness

Benefits

• At Adobe, you will be immersed in an exceptional work environment that is recognized around the world.
• You will also be surrounded by colleagues who are committed to helping each other grow through our unique Check-In approach where ongoing feedback flows freely.
• If you’re looking to make an impact, Adobe's the place for you.
• Discover what our employees are saying about their career experiences on the Adobe Life blog and explore the meaningful benefits we offer.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level