Research Programmer II

About the role

The USC Michelson Center Convergent Science Institute in Cancer (CSI-Cancer) seeks a Research Programmer II to join a team focused on early detection, liquid biopsy, rare-cell detection assays, and the development of novel computational and instrumentation platforms for cancer research. This position is ideal for candidates who want to apply computational science and data engineering expertise to accelerate discoveries in cancer dynamics, trajectory prediction, and treatment response modeling.

Responsibilities

• Develop and document robust computational workflows, testing procedures, and production pipelines that support CSI-Cancer’s high-content single-cell, liquid biopsy, imaging, and multi-omics research initiatives.
• Serve as a technical resource to researchers, assessing computational requirements and aligning advanced computing solutions (HPC, cloud, containerized environments) with cancer research objectives in mathematical oncology, disease modeling, and translational science.
• Collaborate on collaborative research efforts at institutional, regional, and national levels to advance CSI-Cancer’s strategic priorities in cancer detection, trajectory forecasting, and precision oncology.
• Design, deliver, and maintain training programs and workshops that enable researchers and academic programs to optimally leverage high-performance and cloud computing for cancer research applications.
• Partner with interdisciplinary teams of engineers, computational scientists, biologists, and clinicians to implement scalable, reproducible, and compliant research computing solutions while continuously improving processes and technical standards.
• Utilize creativity and innovation to solve complex computational challenges arising from cutting-edge cancer research, and identify opportunities to adapt methods and results to novel scientific applications and future research directions.
• Promote a collaborative and inclusive research environment consistent with USC’s Code of Ethics.
• May supervise junior staff and mentor trainees.

Qualifications

• Deep understanding of HPC, high-performance networking, cloud computing, container technologies (e.g., Docker, Singularity), and scalable research infrastructure.
• Experience supporting computational research in a higher education or research institute setting, particularly for data-intensive scientific projects.
• Ability to translate complex scientific research needs — including cancer biology, multi-omics, imaging, and clinical data analysis — into effective computational and analytical solutions.
• Proficiency in database systems, version control (e.g., Git), workflow management, and large-scale application performance engineering and optimization.
• Experience developing and delivering user training, workshops, or educational programs.
• A Master’s degree in Computer Science, Biomedical Engineering, or a closely related discipline.
• Strong software development skills (e.g., Python, C++/C, R, Java, or comparable) and familiarity with data science/machine learning frameworks.
• Experience with large, complex biological or clinical datasets.
• Demonstrated understanding of advanced computational methods such as contrastive deep learning.
• Knowledge of current data modeling tools and various machine learning techniques and algorithms (e.g., clustering, decision-tree learning, artificial neural networks).
• Proficient use of query languages (e.g., SQL, MDX) and experience working with relational (e.g., MySQL, PostgreSQL) databases.
• Knowledge of statistical concepts and techniques (e.g., distributions, statistical testing, regression).
• Demonstrated knowledge of the research process, including proposal development, data lifecycle management, reproducible workflows, and compliance with federal and sponsor regulations.
• Advanced expertise in Linux/Unix systems and experience building, deploying, and maintaining open-source software in high-performance computing (HPC) environments.
• Experience with parallel and accelerated computing frameworks (e.g., OpenMP, MPI) and familiarity with GPUs and large-scale storage systems.
• Strong communication skills, including the ability to document systems and explain technical concepts clearly to interdisciplinary audiences.
• Proven project management and interpersonal skills, with the ability to prioritize multiple initiatives and collaborate effectively with researchers, engineers, and IT professionals.