Intel’s Visual Technology Team (VTT) develops best in class graphics technology that is a critical part of our major product lines. We are looking for graduate-level research scientist interns to join the Advanced Research and Technology Development team’s “machine learning at scale” research initiative. The opportunity involves performing measurements and analysis of distributed machine learning workloads and developing optimizations to improve training speed of large ML models used in production. We offer the unique opportunity to work across all layers of the hardware and software stack.
Responsibilities will include, but are not limited to:
- Implementing state-of-the-art machine learning models that scale across nodes
- Profiling ML workloads on a distributed GPGPU cluster
- Analyzing bottlenecks and performance trade-offs
- Developing optimizations to speed training
You must possess the below minimum qualifications to be initially considered for this position. Preferred qualifications are in addition to the minimum requirements and are considered a plus factor in identifying top candidates. Experience listed below would be obtained through a combination of your school work/classes/research and/or relevant previous job and/or internship experiences.
Minimum Qualifications that will get you notice:
Masters or Ph.D. candidate in Computer Science, Computer Engineering, Electrical Engineering, or a related field.
Study of Focus must be in one of the following :
- Machine learning (reinforcement learning, natural language processing, recommender systems), distributed systems, and/or parallel computing.
- Applications of deep learning to computer graphics or image processing.
An ideal candidate will have demonstrable educational experience and/or top-tier publications in at least one of the following:
- State-of-the-art machine learning models in reinforcement learning, natural language processing and/or recommender systems
- Data parallelism, model parallelism and/or hybrid parallelism for training of large models and large data sets
- Systems-level optimization of distributed systems (e.g., data movement, network protocols, task schedulers)
- State-of-the-art deep learning models including convolutional neural networks and autoencoders for image processing.
- Signal and image processing with requisite math such as linear algebra, random processes, calculus, and optimization.
- Systems-level optimization of training and inference tasks through combination of NN architecture, h/w scaling, s/w efficiency, etc.
- Computer Graphics
Inside this Business Group
Intel Architecture, Graphics, and Software (IAGS) brings Intel’s technical strategy to life. We have embraced the new reality of competing at a product and solution levelânot just a transistor one. We take pride in reshaping the status quo and thinking exponentially to achieve what’s never been done before. We’ve also built a culture of continuous learning and persistent leadership that provides opportunities to practice until perfection and filter ambitious ideas into execution.
All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance….