The OpportunityJoin Adobe's AI Foundations team within the Search, Discovery & Content AI (SDC) organization and help build the next generation of AI-powered retrieval, matching, ranking, and content understanding systems across Adobe.
Our team develops foundational AI capabilities that connect users with the most relevant content across Acrobat, Adobe Stock, Lightroom, Creative Cloud, enterprise repositories, and emerging agentic AI experiences. We work at the intersection of multimodal foundation models, large-scale retrieval systems, ranking, and generative AI.
As a Staff Machine Learning Engineer (MLE 50), you will design, build, and deploy semantic matching and ranking models that understand text, images, documents, and other content modalities at Adobe scale. Your work will shape how millions of users-and increasingly AI agents-discover and interact with content across Adobe's ecosystem.
What You'll Do- Design, develop, and deploy semantic matching, retrieval, and multi-objective ranking models for multimodal content across Adobe products.
- Build representation learning systems using LLMs, vision-language models, embedding models, and multimodal foundation models to improve relevance and content understanding.
- Develop scalable retrieval and ranking architectures powering search, recommendation, content discovery, and agentic workflows across enterprise and marketplace content.
- Train, fine-tune, and optimize machine learning models using large-scale behavioral, content, and synthetic datasets.
- Build evaluation frameworks, benchmarks, and experimentation methodologies to measure relevance, ranking quality, retrieval effectiveness, and user impact.
- Partner with Adobe Research, product teams, and engineering organizations to translate innovative research into production capabilities.
- Drive the end-to-end lifecycle of ML systems, from data preparation and model development to deployment, monitoring, and continuous improvement.
- Stay ahead of with advances in information retrieval, multimodal AI, representation learning, and agentic systems, bringing new innovations into Adobe products.
What You Need to Succeed- Strong experience building and deploying machine learning systems in production.
- Strong experience developing retrieval, recommendation, ranking, search relevance, or semantic matching systems at scale.
- Deep understanding of modern ML techniques, including representation learning, metric learning, transformer architectures, multi-objective optimization, and large-scale model training.
- Experience working with LLMs, embedding models, vision-language models, or other multimodal foundation models.
- Strong software engineering skills with proficiency in Python and experience building scalable ML services and infrastructure.
- Experience with modern ML frameworks such as PyTorch, TensorFlow, JAX, Hugging Face, Ray, Spark, or equivalent technologies.
- Experience designing and analyzing offline and online evaluations, including A/B testing and relevance metrics.
- Familiarity with distributed training, large-scale data processing, and MLOps standard methodologies.
- Strong problem-solving skills and ability to collaborate across research, engineering, and product teams.
- MS or PhD in Computer Science, Machine Learning, Artificial Intelligence, Information Retrieval, or a related field, or equivalent experience.
Nice to Have- Experience building large-scale search, recommendation, retrieval, or ranking systems.
- Expertise in multimodal representation learning across text, images, documents, video, or other modalities.
- Experience with vector search, embedding-based retrieval, approximate nearest neighbor systems, and retrieval-augmented architectures.
- Familiarity with learning-to-rank techniques, relevance optimization, and search quality evaluation.
- Experience applying foundation models, LLMs, and agentic AI systems to retrieval, ranking, or content understanding problems.
- Publications or open-source contributions in machine learning, information retrieval, search, ranking, recommender systems, or multimodal AI.
About the TeamThe AI Foundations team develops core AI capabilities for content and intent understanding, semantic retrieval, matching, ranking, and intelligent content experiences across Adobe.
Our work spans documents, images, creative assets, enterprise repositories, and marketplace content. We build foundational technologies that enable users and AI agents to discover, understand, and interact with the right content at the right time.
We operate at the intersection of multimodal foundation models, large-scale machine learning systems, information retrieval, and generative AI. Our mission is to transform how content is organized, discovered, and used across Adobe products, helping individuals and organizations unlock more value from their content.
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 $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.
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.
