2026 Intern - Machine Learning Engineer

Adobe
2 LocationsPosted 1 April 2026

Job Description

Our Company Changing the world through digital experiences is what Adobe’s all about. We give everyone—from emerging artists to global brands— everything they need to design and deliver exceptional digital experiences. We’re passionate about empowering people to create beautiful and powerful images, videos, and apps, and transform how companies interact with customers across every screen. We’re on a mission to hire the very best and are committed to creating exceptional employee experiences where everyone is respected and has access to equal opportunity. We realize that new ideas can come from everywhere in the organization, and we know the next big idea could be yours. The Opportunity Adobe is looking for a Machine Learning intern who will apply AI and machine learning techniques to big-data problems to help Adobe better understand, lead and optimize the experience of its customers. By using predictive models, experimental design methods, and optimization techniques, the candidate will be working on the research and development of exciting projects like real-time online media optimization, sales operation analytics, customer churn scoring and management, customer understanding, product recommendation and customer lifetime value prediction. All 2026 Adobe interns will be co-located hybrid. This means that interns will work between their assigned office and home. Interns will be based in the office where their manager and/or team are located, where they will get the most support to ensure collaboration and the best employee experience. Managers and their organization will determine the frequency they need to go into the office to meet priorities. What You’ll Do Develop predictive models on large-scale datasets to address various business problems with statistical modeling, machine learning, and analytics techniques. Develop and implement scalable, efficient, and interpretable modeling algorithms that can work with large-scale data in production systems Collaborate with product management and engineering groups to develop new products and features. What You Need to Succeed Currently enrolled full time and pursuing a Bachelors, Master’s or PhD degree in Computer Science, Computer Engineering; or equivalent experience required with an expected graduation date of December 2026 – June 2027 Good understanding of statistical modeling, machine learning, deep learning, or data analytics concepts. Proficient in one or more programming languages such as Python, Java and C Familiar with one or more machine learning or statistical modeling tools such as R, Matlab and scikit learn Strong analytical and quantitative problem-solving ability. Excellent communication, relationship skills and a team player Ability to participate in a full-time internship between May-September About Adobe Adobe empowers everyone to create through innovative platforms and tools that unleash creativity, productivity and personalized customer experiences. Adobe’s industry-leading offerings including Adobe Acrobat Studio, Adobe Express, Adobe Firefly, Creative Cloud, Adobe Experience Platform, Adobe Experience Manager, and GenStudio enable people and businesses to turn ideas into impact, powered by AI and driven by human ingenuity. Our 30,000 employees worldwide are creating the future and raising the bar as we drive the next decade of growth. We’re on a mission to hire the very best and believe in creating a company culture where all employees are empowered to make an impact. At Adobe, we believe that great ideas can come from anywhere in the organization. The next big idea could be yours. Let’s Adobe together At Adobe, we believe in creating a company culture where all employees are empowered to make an impact. Learn more about Adobe life, including our values and culture , focus on people, purpose and community , Adobe for All , comprehensive benefits programs , the stories we tell , the customers we serve, and how you can help us advance our ... (truncated, view full listing at source)
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