Intern - AI Machine Learning and Software Development
The Data Storage and Memory Devices Group within the Seagate Research Group (SRG) is primarily responsible for performing experiments and simulations to guide the design the future of hard-disk drive based, and other, advanced data storage and memory technologies.
The work performed by this team directly informs the Company’s product roadmaps. Experiments involve electrical testing of state-of-the-art hard disk drive and memory systems to explore critical recording physics phenomena. Modeling and simulation are used to evaluate designs up to 10 years in the future, as well as elucidate experimentally observed phenomena. Machine learning models are also being developed to predict product reliability and inform future materials and design directions.
About the role - you will:
- Develop state-of-the-art AI/ML and data science solutions to solve/optimize technical problems in recording physics or other advanced technology areas
- Utilize big data analytics tools and apply analytical techniques for data retrieval, preparation, and discovery
- Involve in advanced analytics projects and take part in the data science project journey from solution development to deployment
- Implement data science algorithm and utilize big data advanced analytics platform to create applications for stakeholders
- Create visualization output to illustrate the prediction result
- Pursuing a MS, or PhD degree in Computer Science, Computer Engineering, Software Engineering, Electrical engineering, Data Science, Statistics, Applied Math, Physics, or other related areas. Must be enrolled in Fall 2024 classes.
- Passionate about computer programming and data science
- Self-motivated, independent, and a team player with strong communication and interpersonal skills
- Attention to detail and proactive
Your experience includes:
- Programming experience in Python, Keras, Tensorflow, Flask and/or Django
- Experience in GIT, Docker container, Kubernetes, AWS, microservices, CI/CD, DevOps, MLOps
- Good understanding and experience in machine learning and deep learning, e.g., DNN, CNN, Transformer, Reinforcement Learning, Active Learning, GAN, etc.