Staff ML Engineer - Generative & Multimodal AI

Adobe; San Jose (CA); On-site; USD 212,000 - 307,000; Full time

Job summary

Adobe’s Brand AI Services team is seeking a Staff Machine Learning Engineer to design and deliver production-grade generative AI systems powering the Firefly AI Assistant across Creative Cloud and related products. You will lead multimodal AI development, build tool-using agentic workflows, and scale APIs for enterprise use.

Expect to mentor engineers, collaborate with product and research teams, and drive the ML lifecycle from problem formulation to monitoring in a high-traffic environment.

Qualifications

• MS or PhD in Computer Science, Machine Learning, or a related field, or equivalent practical experience.
• 5+ years of experience building and deploying machine learning systems in production.
• Hands‑on experience designing and building agentic AI systems, including tool use, agent orchestration, multi‑step workflows, planning and reasoning, retrieval, memory, or human‑in‑the‑loop systems.
• Experience with agent interoperability and tool integration, including Model Context Protocol (MCP), function/tool calling, or similar frameworks and protocols.
• Expertise in computer vision, generative AI, and/or multimodal machine learning, with hands‑on experience using modern architectures such as transformers, diffusion models, LLMs, or VLMs.
• Solid foundation in probability, statistics, machine learning, and model evaluation.
• Proficiency in Python and experience with machine learning frameworks such as PyTorch.
• Experience designing and building scalable APIs, distributed services, or production ML infrastructure.
• Strong software engineering fundamentals, including data structures, algorithms, testing, code quality, and code reviews.
• Experience with cloud platforms such as AWS or Azure and containerization and orchestration technologies such as Docker and Kubernetes.
• Familiarity with modern AI‑assisted development tools and workflows, including systems such as ChatGPT, Claude, Cursor, or similar tools, and experience using them for development, experimentation, or productivity.

Responsibilities

• Lead the design, development, and deployment of multimodal and generative AI systems spanning vision, language, and other modalities.
• Build and productionize generative AI models and systems, including transformers, diffusion models, LLMs, and vision-language models (VLMs), for content creation, understanding, and transformation.
• Develop and build agentic AI systems that can reason, use tools, interact with models and services, and complete complex multi-step creative workflows.
• Build intelligent capabilities for Firefly AI Assistant and Creative Cloud workflows, helping creative professionals move from intent and ideas to high-quality creative outcomes.
• Develop scalable services and APIs that integrate AI and machine learning capabilities into Adobe products.
• Drive the end-to-end ML lifecycle, including problem formulation, modeling, experimentation, evaluation, deployment, monitoring, and iteration.
• Partner with engineering, product, design, and research teams to translate customer needs into effective ML solutions.
• Improve the performance, scalability, reliability, and quality of AI systems operating in high-traffic production environments.
• Provide technical leadership, mentor engineers, and help raise the engineering and machine learning bar across the team.
• Identify new opportunities to apply generative and agentic AI to real-world challenges for creative professionals and enterprise customers.

Skills

Generative AI

Multimodal ML

Agentic AI

Python

PyTorch

AWS/Azure

Docker

Kubernetes

ML lifecycle

Code reviews

Education

MS/PhD in CS/ML or equivalent

Tools

MCP

REST APIs

Job description

Adobe’s Brand AI Services team is seeking a Staff Machine Learning Engineer to design and deliver production-grade generative AI systems powering the Firefly AI Assistant across Creative Cloud and related products. You will lead multimodal AI development, build tool-using agentic workflows, and scale APIs for enterprise use.

Expect to mentor engineers, collaborate with product and research teams, and drive the ML lifecycle from problem formulation to monitoring in a high-traffic environment.