Applied Materials, Inc. is the global leader in materials engineering solutions used to produce virtually every new chip and advanced display in the world. Our expertise in modifying materials at atomic levels and on an industrial scale enables customers to transform possibilities into reality. At Applied Materials, our innovations make possible the technology shaping the future.

Data-Science/Statistics Internship:  Understanding of statistics, applied math and deep-learning algorithms. Enable deep-learning workloads and models on existing platforms (e.g. GPUs), and work with engineering team to develop various DL algorithmic optimizations such as quantization, pruning, low-rank factorization etc. to evaluate accuracy and performance trade-offs. Support Architecture and Compiler team develop infrastructure to emulate DL workloads on new architectures. Experience with distributed GPU frameworks is a plus. Some understanding of GPU architecture and exposure to performance profiling tools is a big plus.

Ideal Candidate

• Prior experience with DL frameworks (e.g. PyTorch, TensorFlow etc) and DL models (such as ResNet, BERT, Object detection, and Segmentation).
• Understanding of DL optimizations such as quantization (numerics in general), pruning etc.
• Understanding of distributed GPU DL inference, and training frameworks (e.g. Microsoft DeepSpeed, Horovod etc.)
• Profiling and benchmarking DL workload performance on GPUs.

Qualifications

Students [pursuing Bachelor’s degree in Computer Science/ECE/Statistics/Math with coursework in statistics and prior understanding of deep-learning optimizations. Masters or PhD students with prior research experience preferred.

Compensation

$35 - $52

Additional Information

Travel:

Yes, 10% of the Time

Relocation Eligible:

Yes 

Applied Materials is an Equal Opportunity Employer committed to diversity in the workplace. All qualified applicants will receive consideration for employment without regard to race, color, national origin, citizenship, ancestry, religion, creed, sex, sexual orientation, gender identity, age, disability, veteran or military status, or any other basis prohibited by law.