Job description
Job Category: Intern/Student
Primary Location: Santa Clara, CA US
Other Locations
Virtual US and Canada
Intel develops best in class graphics and GPGPU 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 "Applied and Distributed ML" research initiative. The opportunity involves performing measurements and analysis of (distributed) Machine Learning workloads and developing optimizations to improve training and inference speed of (large) ML models used in production. We offer the unique opportunity to work across all layers of the hardware and software stack. Opportunities exist for applying and/or optimizing Machine/Deep Learning models that improve the quality and/or performance of a rasterizing or ray-tracing based graphics pipeline. We are looking for a Machine Learning Research Scientist (Intern) with strong problem solving skills to join the team.
Responsibilities will include a subset from the list below, but are not limited to:
Implementing state-of-the-art machine learning models including those that scale across nodes.
Profiling ML workloads on a distributed GPGPU cluster.
Analyzing bottlenecks and performance trade-offs.
Developing optimizations to speed up training and inference for a variety of models.
Qualifications
Minimum Skills and experience:
Must be pursuing a Ph.D. in Computer Science, Computer Engineering, Electrical Engineering, or a related field. Must have obtained M.S. or completed Qualifying Exam.
Research focus in Machine/Deep Learning (reinforcement learning, natural language processing, recommender systems), distributed systems, and/or parallel computing.
Must have 6+ months of work or educational experience with the following:
Programming skills: PyTorch, Python, TensorFlow or C/C++. PyTorch is preferred.
Data analysis and debugging.
Preferred skills and experience:
An ideal candidate will have demonstrable experience and/or top-tier publications in one or more of the following:
State-of-the-art machine learning models in image/video/graphics processing, 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)
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 level—not 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.
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