Staff Machine Learning Engineer 5

Adobe•San Jose, CA

About The Position

We are looking for a Staff Machine Learning Engineer to join our team of driven machine learning and software engineers. This role covers system design, prompt engineering, ML model evaluation, building data pipelines, prototype creation, and operationalization. The ideal candidate will have a strong background in both classical and deep learning, along with experience developing and deploying production-ready ML and GenAI solutions. If you enjoy shipping impactful, customer-facing features, experimenting with new technologies, and contributing to high-visibility projects with room for creativity and ownership, this role is for you. Our work is dynamic, collaborative, and data-driven.

Requirements

Master’s or Ph.D. in Computer Science, Machine Learning, or a related technical field.
10+ years of experience in machine learning engineering, applied research, or production ML systems.
Strong Python software engineering skills, including system design, clean architecture, testing, CI/CD, version control, and code review best practices.
Experience taking ML-powered features from 0→1 through production and ongoing iteration.
Hands-on experience deploying and monitoring ML models in production environments.
Experience designing or contributing to agentic architectures and multi-agent orchestration systems.
Strong understanding of classical ML, deep learning, and modern Generative AI techniques.
Familiarity with cloud platforms (AWS, GCP, or Azure) for scalable ML deployment.
Solid foundation in data structures, algorithms, and distributed system design.
Comfortable leveraging AI coding agents to accelerate development workflows.
Excellent communication skills and demonstrated technical leadership experience.

Nice To Haves

Experience with Generative AI systems, prompt optimization frameworks, and LLM-as-a-judge / evaluation methodologies.
Deep understanding of Retrieval-Augmented Generation (RAG) and modern NLP pipelines.
Experience with MLOps tooling, experiment tracking, model lifecycle management, and observability frameworks.
Track record of mentoring engineers and influencing technical direction on high-visibility projects.

Responsibilities

Bring a 0→1 product mindset, helping shape ideas into real, measurable impact.
Design, build, and optimize backend services that power ML and Generative AI features.
Develop, evaluate, and deploy ML models using classical, deep learning, and GenAI approaches.
Contribute to agentic systems and orchestration frameworks that enable intelligent, multi-step reasoning and task automation.
Collaborate with cross-functional teams to integrate ML solutions into production workflows.
Analyze and improve the efficiency, accuracy, and scalability of AI-enabled systems.
Stay up to date with advancements in ML, GenAI, and prompt optimization research.
Mentor junior engineers and help grow the team’s technical depth.