Machine Learning Engineer

Adobe Adobe · Enterprise · San Jose, CA

Machine Learning Engineer at Adobe focused on designing, building, and optimizing backend services for ML and GenAI features, including agentic systems and orchestration. The role involves developing, evaluating, and deploying ML models, taking features from 0 to 1, and integrating solutions into production workflows. Requires strong Python software engineering, experience with production ML systems, and familiarity with cloud platforms.

What you'd actually do

  1. Bring a 0→1 product mindset, helping shape ideas into real, measurable impact.
  2. Design, build, and optimize backend services that power ML and Generative AI features.
  3. Develop, evaluate, and deploy ML models using classical, deep learning, and GenAI approaches.
  4. Contribute to agentic systems and orchestration frameworks that enable intelligent, multi-step reasoning and task automation.
  5. Collaborate with cross-functional teams to integrate ML solutions into production workflows.

Skills

Required

  • Master’s or Ph.D. in Computer Science, Machine Learning, or a related technical field.
  • 5+ 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 have

  • 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.

What the JD emphasized

  • 0→1 product mindset
  • agentic systems and orchestration frameworks
  • taking ML-powered features from 0→1 through production and ongoing iteration
  • Experience designing or contributing to agentic architectures and multi-agent orchestration systems

Other signals

  • shipping impactful, customer-facing features
  • experimenting with new technologies
  • contributing to high-visibility projects
  • room for creativity and ownership
  • dynamic, collaborative, and data-driven