Machine Learning Engineer 3

Adobe Adobe · Enterprise · San Jose, CA

Machine Learning Engineer at Adobe to optimize customer experience through predictive and generative models, focusing on autonomous agents and agentic frameworks. The role involves designing, developing, and deploying applications, implementing ML-Ops, and collaborating with research and product teams.

What you'd actually do

  1. Design, develop, and deploy applications powered by predictive and generative models, with a focus on building autonomous agents and using agentic frameworks for adaptive decision-making.
  2. Implement ML-Ops best practices to ensure scalable, reliable, and efficient machine learning workflows.
  3. Develop innovative models in collaboration with Adobe Research.
  4. Engage in the product lifecycle, including architecture, design, deployment, and production operations.
  5. Understand data to make recommendations for the right predictive models, quality metrics, and governance approaches.

Skills

Required

  • MS in Computer Science, Data Science or Statistics with 3+ years of applied AI/ML experience, including developing, evaluating ML models, and deploying models into production or PhD degree in Computer Science, Data Science, or a related field.
  • Deep understanding of statistical modeling, machine learning, or analytics concepts, with a proven track record of solving problems using these methods.
  • Experience in building large-scale data pipelines.
  • Proficiency in one or more programming languages such as Python, Scala, Java, or SQL.
  • Proficiency in ML frameworks such as scikit-learn, SparkML, TensorFlow, or PyTorch.

Nice to have

  • Ability to quickly learn new skills and work in a fast-paced team.
  • Experience working with both research and product teams.
  • Excellent problem-solving and analytical skills.
  • Excellent communication and relationship-building skills.

What the JD emphasized

  • building autonomous agents
  • using agentic frameworks
  • deploying models into production
  • applied AI/ML experience

Other signals

  • building autonomous agents
  • using agentic frameworks for adaptive decision-making
  • ML-Ops best practices
  • deploying models into production