2025 University Graduate - Machine Learning Engineer

Adobe is looking for a Machine Learning Engineer to understand & optimize the experience of Adobe's Digital Experience customers. Partnering with Adobe Research and other business units, the candidate will build products that transform the way companies approach audience creation, journey optimization, and personalization at scale. You will join a diverse, collaborative, lively group of engineers and scientists long established in the ML space. The work is dynamic, fast-paced, creative, and data-driven.

What you'll Do:

• Develop innovative models in partnership with Adobe Research.
• Design and build applications powered by predictive models, including working on traditional engineering problems such as defining APIs, integrating with UIs, deploying on Cloud services, CICD, etc., as well as implementing ML-Ops best practices.
• Understand data to make recommendation for the right predictive models, quality metrics and governance approaches.
• Engage in product lifecycle – architecture, design, deployment, and production operations.

What you need to succeed:

• MS or PhD degree in Computer Science, Data Science or related field required.
• Deep understanding of statistical modeling, machine learning, or analytics concepts, and a track record of solving problems with these methods.
• Experience in or knowledge of building large scale data pipeline.
• Ability to quickly learn new skills and work in a fast-paced team.
• Proficient in one or more programming languages such as Python, Scala, Java, SQL.
• Proficient in ML frameworks such as scikit-learn, SparkML, TensorFlow or PyTorch.
• Excellent problem-solving and analytical skills.
• Excellent communication and relationship building skills.

Adobe is proud to be an Equal Employment Opportunity and affirmative action employer. We do not discriminate based on gender, race or color, ethnicity or national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status, or any other applicable characteristics protected by law.