Staff Information Retrieval Engineer

Our Company

Changing the world through digital experiences is what Adobe’s all about. We give everyone—from emerging artists to global brands—everything they need to design and deliver exceptional digital experiences! We’re passionate about empowering people to create beautiful and powerful images, videos, and apps, and transform how companies interact with customers across every screen. 

We’re on a mission to hire the very best and are committed to creating exceptional employee experiences where everyone is respected and has access to equal opportunity. We realize that new ideas can come from everywhere in the organization, and we know the next big idea could be yours!

 

The Opportunity
 

We are seeking a passionate and technically strong engineer who excels at the intersection of information retrieval, machine learning, and large-scale systems, and will help shape the future of brand-aware AI at Adobe. You will design and optimize systems that develop a deep understanding of an enterprise brand by ingesting brand information and creative assets into a scalable, retrievable brand intelligence platform, backed by data-driven, custom ontologies.
 

You will play a critical role in enabling context-aware, large, multi-modal models to access the most relevant, timely, and high-quality information—driving improved reasoning and brand compliance across enterprise marketing workflows. Your work will directly support high-impact Adobe surfaces such as GenStudio for Performance Marketers (GS PeM), Workfront, and Adobe Experience Manager (AEM), ensuring that businesses can create at scale while staying on brand.
 

What you'll do
 

· Build ingestion pipelines for structured and unstructured data sources

· Implement sparse and dense, semantic and lexical indexing, as well as metadata enrichment strategies

· Contribute to the creation and maintenance of multi-modal brand-specific ontologies and graphs for context linking, disambiguation, and brand-aware creative production

· Ensure data freshness, versioning, and reliability in retrieval systems

· Implement hybrid search strategies to optimize precision and recall for brand-specific contexts using vector and graph databases

· Optimize intelligent query understanding and planning mechanisms for enterprise-grade use cases

· Directly engage with product managers and engineers to align retrieval outputs with creative workflows, with unparalleled scalability, efficiency, and performance

· Monitor retrieval system health with metrics such as accuracy, latency, and fallback rate

· Implement caching, prefetching, and de-duplication strategies to deliver low-latency, high-throughput experiences
 

What you need to succeed
 

· 4+ years of experience in information retrieval, data engineering, or ML infrastructure

· Experience in building and deploying RAG pipelines or semantic search systems using well-known search platforms (Elastic, Lucene, Vespa, Pinecone)

· Expert programming skills in Python, and experience with frameworks such as Haystack, LangChain, or LangGraph

· Extensive expertise in search result evaluation methods

· Familiarity with cloud platforms (Azure, AWS) and containerization/orchestration (Docker, Kubernetes)

· Understanding of ML pipelines, monitoring, and MLOps standards
 

Preferred Qualifications
 

· Experience with graph databases (Neo4j, TigerGraph) or building knowledge graphs

· Experience with streaming platforms such as Apache Flink, Spark Streaming, Kafka Streams

· Background in IR/NLP, search engineering, or cognitive computing

· Degree in Computer Science, Information Systems, or a related field, or equivalent experience

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 $172,500 -- $306,625 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