Machine Learning Engineer, Express AI Foundations

Adobe Adobe · Enterprise · San Jose, CA

Machine Learning Engineer at Adobe Express AI Foundations to build and scale the core AI platform powering creativity across design, imaging, motion, and personalization. The role involves developing production systems for Agentic AI, Create AI, Imaging AI, Motion AI, and Personalization AI, focusing on model integration, inference services, data pipelines, storage, caching, analytics, and evaluation tooling.

What you'd actually do

  1. Contribute to the development of core platform components that support AI experiences in Adobe Express.
  2. Build and improve backend services, microservices, and workflows that connect models, APIs, data systems, and product features.
  3. Help develop data and inference pipelines for training, evaluation, fine-tuning, and deployment of ML models.
  4. Support runtime systems for inference and orchestration with attention to reliability, observability, and performance.
  5. Work on storage, caching, and data-access patterns to improve efficiency, scalability, and cost.

Skills

Required

  • 3+ years of experience in software engineering, backend infrastructure, data systems, ML infrastructure, or related areas.
  • Good understanding of distributed systems fundamentals, backend services, and scalable system design.
  • Experience building or supporting APIs, data pipelines, or event-driven systems.
  • Proficiency in Python, Java, C++, or Go.
  • Familiarity with cloud environments, service deployment, and production engineering practices.

Nice to have

  • Exposure to ML systems or LLM-based applications, including model inference, orchestration, or evaluation, is a plus.
  • Strong problem-solving skills and the ability to work well in a collaborative team environment.
  • Clear communication skills and willingness to learn from cross-functional partners.
  • Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Data Science, or a related technical field.
  • Experience with technologies such as Kafka, Spark, Flink, or similar distributed data frameworks.
  • Exposure to generative AI systems such as LLMs, multimodal models, or diffusion models.
  • Familiarity with MLOps concepts such as experiment tracking, model deployment, or evaluation workflows.
  • Interest in agentic AI concepts such as tool use, task planning, or memory systems.

What the JD emphasized

  • production systems
  • AI platform
  • inference services
  • data pipelines
  • evaluation tooling
  • runtime systems
  • storage
  • caching
  • data-access patterns

Other signals

  • AI platform
  • production systems
  • inference services
  • data pipelines
  • evaluation tooling