Senior / Information Retrieval Engineer (AI/ML), Brand Concierge

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)

• 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

• 4+ years in data engineering, ML infrastructure, or information retrieval
• Experience building and deploying RAG pipelines or semantic search systems
• Strong ML and 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

Our interviews are designed to reflect your own skills and thinking. The use of AI or recording tools during live interviews is not permitted unless explicitly invited by the interviewer or approved in advance as part of a reasonable accommodation. If these tools are used inappropriately or in a way that misrepresents your work, your application may not move forward in the process. At Adobe, we empower employees to innovate with AI - and we look for candidates eager to do the same. As part of the hiring experience, we provide clear guidance on where AI is encouraged during the process and where it’s restricted during live interviews. See how we think about AI in the hiring experience . Expected Pay Range: 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 $172,500 – $306,625 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 $211,800 - $306,625 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

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