Staff Agentic ML Engineer - Photoshop

Adobe•San Jose, CA

About The Position

We are building an advanced AI platform that powers next-generation creative workflows for flagship products like Photoshop and Lightroom. As a Senior Machine Learning Engineer (or Applied Scientist), you will combine deep technical expertise with architectural leadership to design and implement reasoning systems, tool orchestration, and multimodal integrations using cutting-edge large language models (LLMs) and vision-language models (VLLMs).

Requirements

Master's or Ph.D. in Computer Science, Machine Learning, Data Science, or a related field
8+ years of industry ML experience
Demonstrated expertise in LLMs, fine-tuning (SFT, RLHF, DPO, PEFT/LoRA), and agentic system development in production
Deep experience building evaluation frameworks for LLM and multi-agent systems (LLM-as-judge, automated benchmarks, red-teaming)
Proficiency in PyTorch and agentic frameworks: LangChain, LangGraph, MCP, Agent Development Kit (ADK)
Fluency with AI-assisted development tools (e.g. Claude Code, Codex, Cursor) while maintaining independent engineering judgment and critical ownership of technical decisions
Strong foundation in data structures, algorithms, and software engineering principles
Excellent problem-solving and communication skills; comfortable operating in fast-moving, ambiguous environments

Nice To Haves

Experience with multimodal models (vision-language, image generation/editing) is a strong plus

Responsibilities

Lead the development of next-generation AI agents that improve and extend Adobe's product ecosystem
Drive the architecture and implementation of robust agentic systems that combine reasoning, retrieval, tool use, and multimodal capabilities
Own end-to-end LLM fine-tuning pipelines — data curation, training, RLHF/DPO alignment, and evaluation
Design and maintain comprehensive evaluation frameworks to measure agent quality, reliability, and safety
Implement state-of-the-art LLM techniques for specialized creative applications
Mentor and grow engineers and scientists across the team; establish technical best practices
Move fast in an environment where priorities evolve and problems are sometimes loosely defined