ML Engineer, Big Data & Production Analytics

Adobe; New York (NY); On-site; USD 102,000 - 202,000; Full time

Job summary

Adobe is seeking a Machine Learning Engineer to apply AI and machine learning techniques to big-data problems across the customer lifecycle. You will build models for attribution, media mix modeling, budget optimization, personalization and causal/time-series analyses.

The role requires strong foundations in statistics, ML, data mining, and software development, with experience handling large datasets and scalable pipelines. Collaboration with product and engineering teams is essential.

Qualifications

• PhD or MS in Computer Science, Statistics, Electrical Engineering, Applied Math, Operations Research, Econometrics or equivalent experience required with an expected graduation date of 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.
• Proficient in Python, Scala, Java and C
• Familiar with 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 strong team player

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

R

Matlab

scikit learn

Education

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

Tools

SQL

Job description

Adobe is seeking a Machine Learning Engineer to apply AI and machine learning techniques to big-data problems across the customer lifecycle. You will build models for attribution, media mix modeling, budget optimization, personalization and causal/time-series analyses.

The role requires strong foundations in statistics, ML, data mining, and software development, with experience handling large datasets and scalable pipelines. Collaboration with product and engineering teams is essential.