Information Retrieval Engineer

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., 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, * High-recall, low-latency retrieval pipelines that enhance LLM outputs with accurate, relevant context
• Modular, reusable RAG components that can be applied across use cases and domains
• Continuous improvement of retrieval relevance and reasoning alignment through experimentation and monitoring
• Strong collaboration with AI engineers, prompt engineers, and data owners to maintain data quality and user trust

Required:

• 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)

Preferred:

• 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

Our compensation reflects the cost of labor across several U.S. geographic markets, and we pay differently based on those defined markets. The U.S. pay range for this positionis $125,600 -- $234,150 annually. Paywithin this range varies by work locationand may also depend on job-related knowledge, skills,and experience. Your recruiter can share more about the specific salary range for the job location during the hiring process.

In California, the pay range for this position is $161,700 - $234,150

At Adobe, for sales roles starting salaries are expressed as total target compensation (TTC = base + commission), and short-term incentives are in the form of sales commission plans. Non-sales roles starting salaries are expressed as base salary and short-term incentives are in the form of the Annual Incentive Plan (AIP).

In addition, certain roles may be eligible for long-term incentives in the form of a new hire equity award.

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