Computational Scientist

Computational Scientist, Research ComputingEmpire 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).Position SummaryThe Computational Scientist will support AI-driven research across New York State’s public and private research institutions. This hands-on technical role works directly with researchers using Empire AI’s shared infrastructure for machine learning, simulation, data science, and secure computing.Reporting to the Manager, Computational and Scientific Programming, the Computational Scientist will partner with faculty, postdocs, and graduate students to build, optimize, and scale computational workflows across disciplines, from foundational AI model development to large-scale simulations in biomedicine, climate, and materials science.Duties and ResponsibilitiesWork with researchers to build, debug, and tune computational workflows across HPC and AI systemsPort, profile, and optimize GPU-accelerated applications, parallel codes, and distributed machine learning training pipelinesBuild scalable, reproducible workflows using tools such as Slurm, Dask, Apptainer, Snakemake, or NextflowContribute hands-on to the technical components of research projects across partner institutionsPerform data wrangling, model training, and performance benchmarking in collaboration with facultyPackage research code into reusable software modules, containers, and scalable workflowsServe as a go-to technical resource for AI/ML tools, GPU optimization, and data-intensive computingTroubleshoot user jobs and work with system administrators to surface user needs and platform issuesTest and evaluate new software tools and frameworks for readiness, compatibility, and performanceWrite and maintain user documentation, examples, and best-practice guidesDeliver technical workshops or tutorials for domain scientists adopting advanced computing toolsContribute technical sections to grant proposals, publications, white papers, and presentations as neededSupport students and junior researchers through code reviews, office hours, and one-on-one consultationsParticipate in pilot projects and cross-institutional knowledge-sharing initiativesMinimum QualificationsPh.D. in a STEM discipline involving computational research (e.g., Computer Science, Physics, Bioinformatics, Applied Math, Engineering)At least 1 year of experience using or supporting high-performance or research computing systemsStrong proficiency in Python, R, or C/C++Experience with Linux environments and job scheduling systems (e.g., Slurm)Ability to work collaboratively with researchers in a service-oriented environmentPreferred QualificationsHands-on experience with deep learning frameworks (e.g., PyTorch, TensorFlow) and scientific computing tools (e.g., NumPy, scikit-learn, CUDA, MPI)Experience running and scaling workloads on multi-node GPU clustersExperience profiling and optimizing code performance (e.g., Nsight, perf, mixed precision, distributed training strategies)Familiarity with LLM architectures and training techniques (e.g. Transformers, vLLM)Familiarity with research data lifecycle management, FAIR principles, and secure data handling (e.g., HIPAA, NIST 800-171)Background in reproducible research practices, version control (e.g., Git), and containerized computingContributions to open-source scientific software projects or community research toolsCompensationOur 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 $130,000. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience.