2025 University Graduate - Machine Learning Engineer

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!

Adobe is looking for a Machine Learning Engineer 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. Collaborating with the business units, the candidate will be building various products that address challenging business problems through our customers' full lifecycle, from customer analytics to marketing media optimization.

By using statistical and econometric methods, predictive models, experimental design methods, and optimization techniques, the candidate will be working on the research and development of exciting projects like attribution, media mix modeling, budget optimization, personalization, causal analysis, time series analysis.

Ideal candidates will have a strong academic background as well as technical skills including applied statistics, machine learning, data mining, and software development. Familiarity with working with large-scale datasets and big data techniques would be a plus.

• Develop predictive models on large-scale datasets to address various business problems with advanced statistical modeling, machine learning, and analytics techniques.
• Develop and implement scalable and efficient 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

• PhD or MS degree in Computer Science, Statistics, Electrical Engineering, Applied Math, Operations Research, Econometrics or equivalent experience required
• 0-2+ years of experience in specific skill/field(s)
• Deep understanding of statistical modeling, machine learning, deep learning, or analytics concepts, and a track record of solving problems with these methods.
• Proficient in one or more programming languages such as Python, Scala, Java and C
• Familiar with one or more machine learning or statistical modeling tools such as R, Matlab and scikit learn
• Knowledge and experience of working with relational databases and SQL
• Strong analytical and quantitative problem-solving ability.
• Excellent communication, relationship skills and a great teammate.

At Adobe, for sales roles starting salaries are expressed as total target compensation (TTC = base + commission), and short-term incentives are in the form of sales commission plans. Non-sales roles starting salaries are expressed as base salary and short-term incentives are in the form of the Annual Incentive Plan (AIP).

In addition, certain roles may be eligible for long-term incentives in the form of a new hire equity award.

Adobe is proud to be anEqual Employment Opportunityand 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.Learn more.

Adobe aims to make Adobe.com accessible to any and all users. If you have a disability or special need that requires accommodation to navigate our website or complete the application process, emailaccommodations@adobe.comor call (408) 536-3015.

Adobe values a free and open marketplace for all employees and has policies in place to ensure that we do not enter into illegal agreements with other companies to not recruit or hire each other's employees.