Adobe
Overview
Join to apply for the
Information Retrieval Engineer
role at
Adobe . The Opportunity
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. What You’ll Do RAG System Design — Architect and deploy scalable retrieval pipelines using vector databases (e.g., FAISS, Weaviate, Pinecone, Qdrant)
RAG System Design — Implement semantic search infrastructure and hybrid retrieval systems (semantic + keyword)
Data Processing & Ingestion — Build ingestion pipelines for both structured and unstructured data sources
Data Processing & Ingestion — 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
Retrieval Optimization — 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
Knowledge Enhancement — 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)
Reasoning Support — 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
Performance Monitoring — Implement caching, prefetching, and deduplication strategies to optimize system responsiveness
What You Need To Succeed 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 Qualifications 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
Degree in Computer Science, Information Systems, or a related field
Compensation and Benefits
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 position is $162,000 -- $301,200 annually. Pay within this range varies by work location and 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. 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. State-Specific Notices
California:
Fair Chance Ordinances — Adobe will consider qualified applicants with arrest or conviction records for employment in accordance with state and local laws and “fair chance” ordinances. Colorado:
Application Window Notice — There is no deadline to apply to this job posting because Adobe accepts applications for this role on an ongoing basis. The posting will remain open based on hiring needs and position availability. Massachusetts:
Massachusetts Legal Notice — It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. Adobe is proud to be an Equal Employment Opportunity employer. We do not discriminate based on gender, race or color, ethnicity or national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status, or any other applicable characteristics protected by law. Learn more. If you require accommodations to navigate the website or complete the application process, please email accommodations@adobe.com or call (408) 536-3015.
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Join to apply for the
Information Retrieval Engineer
role at
Adobe . The Opportunity
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. What You’ll Do RAG System Design — Architect and deploy scalable retrieval pipelines using vector databases (e.g., FAISS, Weaviate, Pinecone, Qdrant)
RAG System Design — Implement semantic search infrastructure and hybrid retrieval systems (semantic + keyword)
Data Processing & Ingestion — Build ingestion pipelines for both structured and unstructured data sources
Data Processing & Ingestion — 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
Retrieval Optimization — 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
Knowledge Enhancement — 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)
Reasoning Support — 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
Performance Monitoring — Implement caching, prefetching, and deduplication strategies to optimize system responsiveness
What You Need To Succeed 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 Qualifications 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
Degree in Computer Science, Information Systems, or a related field
Compensation and Benefits
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 position is $162,000 -- $301,200 annually. Pay within this range varies by work location and 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. 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. State-Specific Notices
California:
Fair Chance Ordinances — Adobe will consider qualified applicants with arrest or conviction records for employment in accordance with state and local laws and “fair chance” ordinances. Colorado:
Application Window Notice — There is no deadline to apply to this job posting because Adobe accepts applications for this role on an ongoing basis. The posting will remain open based on hiring needs and position availability. Massachusetts:
Massachusetts Legal Notice — It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. Adobe is proud to be an Equal Employment Opportunity employer. We do not discriminate based on gender, race or color, ethnicity or national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status, or any other applicable characteristics protected by law. Learn more. If you require accommodations to navigate the website or complete the application process, please email accommodations@adobe.com or call (408) 536-3015.
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