Machine Learning Engineer

Adobe Adobe · Enterprise · San Jose, CA

Machine Learning Engineer to optimize customer experience orchestration by building products that transform audience creation, journey optimization, and personalization. Focus on developing innovative models, designing and deploying applications with predictive and generative models, building autonomous agents, and implementing ML-Ops best practices.

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.
  • Experience working with both research and product teams.

Nice to have

  • Ability to quickly learn new skills and work in a fast-paced team.
  • Excellent problem-solving and analytical skills.
  • Excellent communication and relationship-building skills.

What the JD emphasized

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

Other signals

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