Carnegie Mellon University is a private, global research university that stands among the world’s most renowned education institutions. With ground-breaking brain science, path-breaking performances, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the curious to deliver work that matters, your journey starts here!
The Pittsburgh Supercomputing Center (PSC) — a joint research center of Carnegie Mellon University and the University of Pittsburgh — has been at the forefront of advanced computing since its founding in 1986. For nearly 40 years, PSC has provided university, government, and industry researchers nationwide with access to some of the most powerful systems for high-performance computing (HPC), artificial intelligence / machine learning (AI/ML), high-speed communications, and large-scale data storage for unclassified research. Our work drives discoveries across a wide range of disciplines, including AI/ML, data science, medical imaging, weather modeling, cell biology, and genomics.
As a Computational Scientist, you will take a leadership role in advancing research on state-of-the-art high-performance computing platforms. Building on deep technical expertise, you will not only provide hands-on support to internal and external users, but also lead teams, coordinate complex projects, and shape strategies that enable large-scale scientific discovery. You’ll deliver in-depth consultation, guide proposal development, and design and deliver advanced technical training. In this role, you will ensure that allocations are optimized, workflows are efficient, and research outputs meet the highest standards. By advising on system configurations, best practices, and performance policies, you will help set the direction for how computational science is conducted across diverse research domains.
This is an exciting chance to contribute at the leading edge of computational and data science. PSC’s current projects include leading major NIH data initiatives (HuBMAP: hubmapconsortium.org, SenNet: sennetconsortium.org, BIL: brainimagelibrary.org); national NSF research computing infrastructure (Bridges-2: psc.edu/resources/bridges-2, ACCESS: access-ci.org, NAIRR: nsf.gov/cise/national-ai.jsp); innovative AI/ML resources (Neocortex: psc.edu/resources/neocortex); and specialized supercomputers in collaboration with industry partners (Anton 3: psc.edu/resources/anton).
Core Responsibilities:
- Collaborate with research teams to develop, install, document, port, debug, and optimize application-level research software within your scientific domain.
- Lead or coordinate resolution of complex technical issues referred by the PSC helpdesk, ensuring timely and high-quality solutions.
- Stay at the forefront of high-performance computing (HPC), AI/ML, and large-scale data technologies, and apply this expertise to advance research outcomes.
Adaptability, excellence, and passion are vital qualities within Carnegie Mellon University. We are in search of a team member who can effectively interact with a varied population of internal and external partners at a high level of integrity. We are looking for someone who shares our values and who will support the mission of the university through their work.
Qualifications:
- Bachelor’s degree or equivalent, with a strong emphasis in scientific computing.
- 3–5 years’ experience in the following areas:
- Building, installing, maintaining, debugging, and troubleshooting application-level software in UNIX environments.
- Using shell scripting and batch schedulers such as Slurm.
- Managing competing priorities across diverse stakeholders, demonstrating excellent interpersonal, oral, and written communication skills.
- A combination of education and relevant experience from which comparable knowledge is demonstrated may be considered.
Preferred Qualifications
- Ph.D. degree.
- Demonstrated expertise in computational biophysics and molecular dynamics (algorithms and current practice).
- Demonstrated expertise in AI/ML.
- Proven track record in application performance optimization.
- Advanced experience with parallel programming models (e.g., OpenMP, MPI, CUDA).
- Extensive experience parallelizing serial computational applications.
- Strong background in data analytics.
- Proficiency with one or more of Python (NumPy/SciPy/Pandas), R, MATLAB, etc.
- Familiarity with containers such as Singularity, Apptainer, or Docker.
Requirements:
- Successful background check
Are you interested in this exciting opportunity? Please apply