Research Scientist

Stony Brook University • Town of Brookhaven, NY

14h

About The Position

The Institute for Advanced Computational Science (IACS) at Stony Brook University is seeking an early-career Research Scientist to join AMA27/Kitsune, a new National Science Foundation-funded Tier-1 supercomputing center and central resource of the Stony Brook Supercomputing Center. The center supports large-scale research in AI and machine learning, data science, computational physics, climate science, biomedical research, materials science, and other data- and compute-intensive fields.

The Research Scientist will work with faculty, students, researchers, and research-computing staff across scientific disciplines, with particular emphasis on AI/ML workloads and research workflows. The role offers hands-on experience with AI/ML software, GPU and high-performance computing, and large-scale research computing. The scientist will help researchers run reliable and reproducible workflows on the supercomputing platform while developing expertise in AI/ML and HPC. Work must follow the policies and procedures of SBSC, IACS, Stony Brook University, and the National Science Foundation.

Employment is through the Research Foundation of SUNY and is subject to its policies and procedures, sponsor guidelines, and funding availability. This is an FLSA-exempt position and is not eligible for overtime. The employer is unable to sponsor work visas. Applicants should include a resume/CV and cover letter with their online application. The starting salary range is referenced in the posting, but no range amount is provided in the supplied text.

Requirements

• Ph.D. or foreign equivalent in computer science, computational science, data science, applied mathematics, engineering, physical or life sciences, or a closely related field.
• Three years of research experience using computational, data-driven, or AI/ML methods, including at least one year of research experience after attaining a doctoral degree or foreign equivalent.
• Hands-on experience applying machine learning or artificial intelligence to research or technical problems, including model training, evaluation, or deployment.
• Programming experience in Python using scientific computing, data analysis, or machine learning libraries.
• Experience with at least one AI/ML framework or ecosystem, such as PyTorch, TensorFlow, JAX, scikit-learn, Spark/MLlib, or Hugging Face.
• Experience with Linux-based research computing or high-performance computing, including command-line tools and batch or interactive workflows.
• Strong written and verbal communication skills and ability to work with researchers across multiple disciplines.

Nice To Haves

• Familiarity with parallel or accelerator-based computing, including GPUs, distributed computing, MPI, OpenMP, CUDA, ROCm, or related technologies.
• Experience using GPUs or other accelerators for machine learning, deep learning, or scientific computing.
• Experience with generative AI or large language models, including deploying, running, or optimizing LLMs on local GPU or CPU hardware.
• Experience with distributed or multi-GPU model training, inference, profiling, or performance optimization.
• Experience programming in high-performance computing languages such as C, C++, or Fortran.
• Familiarity with research-computing software and workflow tools such as Slurm, containers, Conda, or Spack.
• Experience with reproducible machine learning workflows, including data preparation, model training, evaluation, and software-environment management.
• Experience developing technical documentation, tutorials, workshops, or other training materials for researchers and students.
• Experience contributing to collaborative research projects, open-source software, publications, or research proposals.

Responsibilities

• Support AI/ML and scientific applications on the supercomputing platform by installing and configuring software, assisting with model training and inference, debugging problems, benchmarking workloads, analyzing performance, and helping users use GPU and other accelerator resources effectively.
• Work with faculty, researchers, students, and research-computing staff to develop and improve AI/ML, data science, and computational workflows.
• Assist with data preparation, reproducibility, scaling, and efficient use of shared computing resources.
• Provide user support and training through technical consultations, documentation, tutorials, office hours, workshops, and presentations.
• Teach researchers to use AI/ML frameworks, GPU-enabled computing, reproducible software environments, the supercomputing platform, and research-computing tools effectively.
• Test, troubleshoot, and maintain research software environments, including machine learning libraries, containers, compilers, schedulers, and related tools.
• Develop and maintain technical documentation and web resources.
• Learn and apply SBSC tools and operational practices, and follow applicable requirements for research activities and data management.
• Contribute to project coordination, reports, presentations, publications, and research or training proposals under the guidance of senior staff and faculty.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree