Job Details
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.
Key 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
Qualifications
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
Success in This Role Looks Like:
- 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
About Adobe
Adobe empowers everyone to create through innovative platforms and tools that unleash creativity, productivity and personalized customer experiences. Adobe’s industry-leading offerings including Adobe Acrobat Studio, Adobe Express, Adobe Firefly, Creative Cloud, Adobe Experience Platform, Adobe Experience Manager, and GenStudio enable people and businesses to turn ideas into impact, powered by AI and driven by human ingenuity.
Our 30,000+ employees worldwide are creating the future and raising the bar as we drive the next decade of growth. We’re on a mission to hire the very best and believe in creating a company culture where all employees are empowered to make an impact. At Adobe, we believe that great ideas can come from anywhere in the organization. The next big idea could be yours.
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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 position is $125,600 -- $234,150 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.
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.
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
If this role is open to hiring in Colorado (as listed on the job posting), the application window will remain open until at least the date and time stated above in Pacific Time, in compliance with Colorado pay transparency regulations. If this role does not have Colorado listed as a hiring location, no specific application window applies, and the posting may close at any time based on hiring needs.
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.