Principal Applied Scientist

Adobe

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
San Jose, CA
Employment
Full-time
Posted
137 days ago
Freshness
Confirmed live yesterday

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Salary context

How this pay compares to similar roles

Similar $225k
$145k $289k
below market most similar roles pay here above market

This listing doesn't post a salary. Most similar roles pay $193,220–$256,850.

Based on 240 similar postings.

Employer

About Adobe

Adobe Inc. is a global software company known for creative and multimedia software products including Photoshop, Illustrator, Acrobat, and its cloud-based Creative Cloud and Document Cloud suites. Industry: Creative & Digital Experience Software

Adobe currently has 282 open roles on FindRole.

Listed pay typically runs $148,100–$282,100 across 76 roles with salary data.

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At a glance

TL;DR · Principal Applied Scientist

The Principal Applied Scientist joins the Search, Discovery, and Content AI team to advance knowledge-grounded AI. This role involves architecting next-generation graph-driven search, hybrid retrieval, and grounded generative systems that integrate knowledge graphs, multimodal embeddings, and ranking systems. You will build large-scale systems connecting queries, entities, and multimodal signals into coherent semantic architectures while defining evaluation frameworks for intent accuracy and entity grounding fidelity. Key responsibilities include establishing modeling standards, mentoring senior scientists, and driving technical direction for graph-grounded RAG and agentic systems. The position requires deep expertise in NLP, information retrieval, and LLM systems, specifically utilizing graph embeddings, GNNs, and hybrid graph-LLM architectures. You will solve complex problems regarding how structured knowledge and foundation models work together to understand intent and creative content.

What does a Applied Scientist earn?

Median $208800 from 61 postings across 10 companies.

See salary data

What you'll do

  • Architect next-generation graph-driven search and hybrid retrieval systems.
  • Build large-scale knowledge-grounded systems connecting queries, entities, and multimodal signals.
  • Develop hybrid neural-symbolic retrieval and ranking models.
  • Implement graph-grounded RAG and agentic systems.
  • Define evaluation frameworks for intent accuracy, entity grounding, and semantic relevance.
  • Establish modeling and experimentation standards across multiple engineering teams.
  • Mentor senior scientists and provide cross-organizational technical direction.

What we're looking for

  • MS in Computer Science, AI, ML, or related field (PhD or equivalent experience preferred).
  • 10+ years of experience building and deploying large-scale AI/ML systems.
  • Deep expertise in knowledge graphs, information retrieval, NLP/intent modeling, multimodal learning, or LLM/RAG systems.
  • Proven track record of delivering production-grade semantic platforms.
  • Experience designing ontologies or evolving domain taxonomies (preferred).
  • Familiarity with graph embeddings, GNNs, or hybrid graph–LLM architectures (preferred).
  • Experience with entity linking, semantic parsing, or large-scale indexing systems (preferred).
  • History of cross-organizational technical influence (preferred).

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