Machine Learning Engineer
Adobe
This job at a glance
- Who can apply
- New grads and up to 2 years
- Must know
- Machine learningMarketingStatisticsData structures & algorithmsProduct managementBudgeting
- Work
- On-site in Austin
- Pay
- Not stated by the employer.
- Skill demand
- Machine learning57%Statistics18%Data structures & algorithms18%Marketing3%of 100 similar entry-level jobs posted in the US this week
- Listed
- Nov 9, 2026 · still open
Sign in free to see which of these skills your resume already shows.
About the Role
Adobe is seeking a Machine Learning Engineer to apply AI and machine learning techniques to big-data problems. This role will focus on understanding, leading, and optimizing customer experiences by partnering with business units to build products that address challenging business problems across the customer lifecycle, from customer analytics to marketing media optimization.
Key Responsibilities
- Develop predictive models on large-scale datasets using advanced statistical modeling, machine learning, and analytics techniques.
- Develop and implement scalable and efficient modeling algorithms for production systems.
- Collaborate with product management and engineering groups to develop new products and features.
- Work on research and development of projects including attribution, media mix modeling, budget optimization, personalization, causal analysis, and time series analysis.
Requirements & Tech Stack
- PhD or MS in Computer Science, Statistics, Electrical Engineering, Applied Math, Operations Research, Econometrics or equivalent experience.
- Expected graduation date: December 2026 – June 2027.
- 0-2+ years of experience in specific relevant skills.
- Deep understanding of statistical modeling, machine learning, deep learning, or data mining concepts.
- Proficient in one or more programming languages: Python, Scala, Java, C.
- Familiar with one or more machine learning or statistical modeling tools: R, Matlab, scikit-learn.
- Knowledge and experience with relational databases and SQL.
- Strong analytical and quantitative problem-solving ability.
- Excellent communication, relationship skills, and a strong team player.
- Familiarity with working with large-scale datasets and big data techniques is a plus.