Manager, Computational Scientific Programming, Research Computing

Overview Manager, Computational and Scientific Programming, Research Computing Empire AI is establishing New York as the national leader in responsible artificial intelligence. Backed by a consortium of top academic and research institutions including Columbia, Cornell, NYU, CUNY, RPI, SUNY, Rochester Schools, Mount Sinai, Simons Foundation, and the Flatiron Institute. By leveraging the state’s rich academic resources and research institutions, Empire AI is driving innovation in fields like medicine, education, energy, and climate change, all while giving New York’s researchers access to computing resources that are often prohibitively expensive and only available to big tech companies, fueling statewide innovation, driving economic growth, and preparing a future-ready AI workforce to tackle society’s most complex challenges. The initiative is funded by $500+ million in public and private investments, State Capital Grant, Academic Institutions, Simons Foundation, Flatiron Institute, and Tom Secunda (Co-Founder of Bloomberg). Responsibilities The Manager, Computational and Scientific Programming will support AI-driven research across New York State’s public and private research institutions. This position serves as a key technical partner to researchers leveraging Empire AI’s shared infrastructure for machine learning, simulation, data science, and secure computing. Reporting to the Director, AI Research Computing, the Manager, Computational and Scientific Programming will work directly with faculty, postdocs, and graduate students to design, optimize, and scale computational workflows that span disciplines—from foundational AI model development to large-scale simulations in biomedicine, climate, and materials science. The role also contributes to strategic initiatives in support of workforce development, proposal writing, and infrastructure planning. Duties And Responsibilities Collaborate with researchers to design, implement, and tune computational workflows across HPC and AI systems Support GPU-accelerated applications, parallel computing, and distributed machine learning training pipelines Enable scalable, reproducible workflows using tools such as Slurm, Dask, Apptainer, Snakemake, or Nextflow Partner with research teams across institutions to co-develop technical components of research projects Act as a technical co-PI or collaborator on funded projects, contributing to research design and implementation Assist with data wrangling, model training, and performance benchmarking in collaboration with faculty Contribute to the preparation of grant proposals by drafting technical narratives, budget justifications, and cyberinfrastructure plans Co-author or support preparation of publications, white papers, and presentations Help translate research outputs into reusable software modules or scalable workflows Provide subject matter expertise in AI/ML tools, GPU optimization, and data-intensive computing Work with system administrators and architects to identify user needs and ensure platform alignment Evaluate new software tools and frameworks for readiness, compatibility, and performance Provide informal mentoring to junior researchers, postdocs, and students working on computational projects Deliver technical workshops or tutorials for domain scientists adopting advanced computing tools Contribute to cross-institutional knowledge-sharing and training initiatives Participate in strategic infrastructure planning or pilot projects Contribute to institutional initiatives in responsible AI, compliance, or interdisciplinary data science Minimum Qualifications Ph.D. in a STEM discipline involving computational research (e.g., Computer Science, Physics, Bioinformatics, Applied Math, Engineering) 5+ years of experience supporting or conducting research using HPC, AI/ML, or large-scale data infrastructure Demonstrated ability to support researchers in a collaborative, service-oriented environment Proficiency with programming languages such as Python, R, or C/C++ Experience with Linux environments and job scheduling systems (e.g., Slurm) Preferred Qualifications Advanced knowledge of deep learning frameworks (e.g., PyTorch, TensorFlow) and scientific computing tools (e.g., NumPy, scikit-learn, CUDA, MPI) Experience developing scalable workflows for multi-node GPU clusters Familiarity with research data lifecycle management, FAIR principles, and secure data handling (e.g., HIPAA, NIST 800-171) Experience writing or supporting competitive federal proposals (e.g., NSF, NIH, DOE) Background in reproducible research practices, version control (e.g., Git), and containerized computing Contributions to open-source scientific software projects or community research tools Compensation Our compensation reflects the cost of labor across several US geographic markets. The base pay and target total cash for this position range from $100,000 to $200,000. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. #J-18808-Ljbffr Research Computing Teams