ML Engineer: Big-Data Predictive Modeling & Personalization

Adobe Inc.; San Jose (CA); On-site; USD 140,000 - 202,000; Full time

Job summary

Adobe is seeking a Machine Learning Engineer to apply AI and ML to big-data problems, helping optimize the customer experience across the full lifecycle. You will build predictive models and scalable algorithms, collaborating with product management and engineering to deploy new features.

The ideal candidate has a strong academic foundation and hands-on experience in statistics, ML, data mining, and software development, with familiarity in working with large-scale datasets.

Qualifications

• PhD or MS in Computer Science, Statistics, Electrical Engineering, Applied Math, Operations Research, Econometrics or equivalent experience required with an 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, and a track record of solving problems with these methods.

Responsibilities

• 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.

Skills

Python

Scala

Java

C

Machine learning

Statistical modeling

Data mining

Education

MS or PhD in Computer Science / Statistics / Electrical Engineering / Applied Math / Operations Research / Econometrics

Tools

scikit-learn

R

Matlab

Job description

Adobe is seeking a Machine Learning Engineer to apply AI and ML to big-data problems, helping optimize the customer experience across the full lifecycle. You will build predictive models and scalable algorithms, collaborating with product management and engineering to deploy new features.

The ideal candidate has a strong academic foundation and hands-on experience in statistics, ML, data mining, and software development, with familiarity in working with large-scale datasets.