You are viewing a preview of this job. Log in or register to view more details about this job.
Email

Pittsburgh Supercomputing Center is a joint computational research center with Carnegie Mellon University and the University of Pittsburgh. Established in 1986, PSC is supported by several federal agencies, the Commonwealth of Pennsylvania and private industry.

Position Overview
We are seeking a motivated ML Research Intern to join our AI and Big Data group. This internship offers the opportunity to work on state-of-the-art projects utilizing Neocortex and Bridges-2, with a focus on scalable machine learning, distributed training, and application development in science and engineering domains. You will collaborate with an interdisciplinary team of researchers, engineers, and domain scientists, contributing to impactful research and development.

Key Responsibilities

  • Conduct research and experiments on ML methods using Neocortex, including network architecture search and hyperparameter optimization.
  • Collaborate with PSC researchers to apply AI/ML techniques to real-world challenges in areas such as life sciences, physics, engineering, and more.
  • Optimize and adapt machine learning frameworks for heterogeneous architectures (e.g., CPUs, GPUs, WSE).
  • Develop and implement custom ML models or refine existing models for high-performance computing environments.
  • Perform benchmarking and scalability studies to evaluate ML model performance on Neocortex.
  • Document your work, including research findings, code, and technical reports, ensuring reproducibility.
  • Present project outcomes in team meetings and, if applicable, to broader audiences at workshops or conferences.

Responsibilities may include:

  • Troubleshooting models during training/evaluation
  • Evaluating the performance of models
  • Presenting/disseminating results in papers/conferences 
  • Researching datasets and use cases 

Our internships offer the opportunity to:

  • Gain valuable experience and knowledge in research computing.
  • Network with leaders in academia and industry to form valuable relationships.
  • Publish in peer-reviewed journals and at prominent conferences.
  • Gain experience on HPC
  • Apply AI/ML to domain specific applications
  • Gain experience on a novel AI accelerator          

Successful candidates will meet the following qualifications:
Required:

  • Currently pursuing a degree (Bachelor’s, Master’s, or Ph.D.) in Computer Science, Data Science, Machine Learning, or a related field. Other examples of relevant majors are Physics, Mathematics, ECE, …
  • Interests in applying AI/ML to the domain specific problems.
  • Strong programming skills in Python and experience with ML libraries/frameworks such as TensorFlow, PyTorch, or Scikit-learn.
  • Knowledge of machine learning concepts, including supervised/unsupervised learning, deep learning, and optimization techniques.
  • Familiarity with high-performance computing environments and distributed systems.
  • Excellent communication and collaboration skills.
  • Excellent problem-solving skills.

Preferred:

  • Familiarity with GPU programming or accelerated computing frameworks (e.g., CUDA, ROCm).
  • Knowledge of Neocortex or similar AI-specific supercomputing systems.
  • Background in scientific computing or experience working on interdisciplinary projects.
  • Proven ability to work independently and manage research deliverables in a timely manner.

On-campus housing is available as part of compensation if needed.