Staff Machine Learning Engineer

Adobe•San Jose, CA

1d•$172,500 - $306,625

About The Position

Join Adobe’s Brand AI Services team and help define the future of AI-powered creativity. We are building multimodal and agentic AI systems that enable marketers and creative professionals to ideate, create, understand, and transform content through intelligent AI-powered workflows. As a Staff Machine Learning Engineer, you will design and deliver production-grade generative and agentic AI systems that power Adobe Firefly AI Assistant and experiences across Creative Cloud, Adobe Express, GenStudio, and more. You’ll work at the intersection of computer vision, generative AI, multimodal learning, and agentic AI, building systems that understand creative intent, reason across multimodal content, use tools, and orchestrate complex creative workflows. This is a high-impact role focused on building the next generation of AI experiences for millions of creative professionals.

Requirements

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 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 systems such as ChatGPT, Claude, Cursor, or similar tools, and experience using them for development, experimentation, or productivity.

Nice To Haves

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.

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.