APPLIED AIx ENGINEER (NEW COLLEGE GRADUATE)
Semiconductors are ubiquitous with our daily lives, and nearly every device has components that went through an Applied Materials tool during manufacturing. It is very likely the device you're reading this on has components that moved through our systems. To meet the growing demand for semiconductors, Applied Materials deploys fleets of thousands of systems around the globe at our customer’s Fabs that perform chip manufacturing, one atomic layer at a time! Applied Materials’ products need to operate 24x7 with reliable and repeatable process outcomes at the nanoscale: same quality, each time. Given the complexity of the materials chemistry, associated control systems and terabytes of data coming out, this can only be achieved with cutting-edge sensors, smart algorithms, and data-analytics software: precisely what we develop and productize in Applied AIx.
Our team delivers software, novel sensors, and advanced analytics-based software solutions to increase the speed of R&D and improve our product performance for our customers at scale. If you have a passion for data, software, controls, and deploying products you’ll fit right in. We need individuals who are eager to learn, thoughtful, and ready to change how an entire industry approaches solving problems.
Positions Needed
- Systems Engineers
- Software Engineers
- Data Scientists
- Machine Learning Engineers
- Analytics Engineers
- Product Managers
- Electrical Engineers
Minimum Qualifications
- Degree in quantitative field (Computer Science, Engineering, Statistics, Chemical Engineering, Mechanical Engineering, Electrical Engineering, or equivalent)
- Understanding of physical systems, signals processing, sensors, semiconductors, manufacturing, microfluidics
- Experience with machine learning, probability, and statistical data analysis techniques, such as regression, time series analysis, hypothesis testing, classification, or clustering
- Experience performing data extraction, cleaning, analysis, and visualization for medium to large datasets
- Fluency with at least one programming language (Python, C++) and writing SQL queries
- Experience with scientific computing packages such as scikit-learn, numpy, scipy, pandas, dplyr, or ggplot2
- Ability to visualize and communicate complex, physics, data, and/or software to a diverse audience
- Ability to quickly learn and apply physics, data science, and/or software concepts