Adobe is building AI capabilities that help creative professionals go from ideas to high-quality outcomes, and this role sits on the Brand AI Services team supporting that mission. As a Staff Machine Learning Engineer, you will design and deliver production-grade multimodal and agentic generative AI systems powering Adobe Firefly AI Assistant and Creative Cloud workflows in high-traffic environments.

This position focuses on scaling the full machine learning lifecycle, from problem formulation through modeling, evaluation, deployment, monitoring, and iteration. You will also provide technical leadership, mentor engineers, and partner across engineering, product, design, and research to translate customer needs into effective ML solutions.

What you’ll do

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 agentic AI systems that can reason, use tools, interact with models and services, and complete complex multi-step creative workflows.
Create intelligent capabilities for Firefly AI Assistant and Creative Cloud workflows that help users 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.
Collaborate with engineering, product, design, and research teams to turn customer needs into effective ML solutions.
Improve performance, scalability, reliability, and quality of AI systems running in production.
Provide technical leadership and mentor engineers to 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.

Required qualifications

MS or PhD in Computer Science, Machine Learning, or 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 areas such as 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 ChatGPT, Claude, Cursor, or similar tools, and experience using them for development, experimentation, or productivity.

Technologies

Python
PyTorch
Transformers
Diffusion models
LLMs
VLMs
Model Context Protocol (MCP)
Function/tool calling
AWS
Azure
Docker
Kubernetes
ChatGPT
Claude
Cursor

Nice to have

Experience building production agentic AI platforms or multi-agent systems, including agent evaluation, observability, reliability, or safety.
Experience with multimodal learning across video, audio, or 3D data.
Background in video understanding or generation, temporal modeling, or streaming ML systems.
Experience fine-tuning, adapting, or optimizing large-scale foundation models.
Knowledge of AI evaluation, safety, and responsible AI practices.
Experience with agent frameworks, orchestration platforms, retrieval systems, or enterprise knowledge integration.
Experience building AI-powered tools or workflows for creative professionals, content creation, or creative applications.
Contributions to research, open-source projects, or applied machine learning innovation.

Interview AI use guideline

The use of AI or recording tools during live interviews is not permitted unless explicitly invited by the interviewer or approved in advance as part of a reasonable accommodation.

Location and compensation

Location: San Jose, CA (onsite)
Expected U.S. pay range: $172,500 - $306,625 per year
California pay range: $211,800 - $306,625 per year

Staff Machine Learning Engineer

Job Description