Computational Scientist

Empire AI; New York (NY); On-site; USD 95,000 - 140,000; Full time

Job summary

Empire AI in New York State is seeking a Computational Scientist to support AI-driven research across public and private institutions, leveraging Empire AI's shared infrastructure for machine learning, simulation, data science, and secure computing.

Reporting to the Manager of Computational and Scientific Programming, you will collaborate with faculty, postdocs, and students to build, optimize, and scale computational workflows across disciplines, from foundational AI model development to

Responsibilities

• Work with researchers to build, debug, and tune computational workflows across HPC and AI systems
• Port, profile, and optimize GPU-accelerated applications, parallel codes, and distributed machine learning training pipelines
• Build scalable, reproducible workflows using tools such as Slurm, Dask, Apptainer, Snakemake, or Nextflow
• Contribute hands‑on to the technical components of research projects across partner institutions
• Perform data wrangling, model training, and performance benchmarking in collaboration with faculty
• Package research code into reusable software modules, containers, and scalable workflows
• Serve as a go‑to technical resource for AI/ML tools, GPU optimization, and data‑intensive computing
• Troubleshoot user jobs and work with system administrators to surface user needs and platform issues
• Test and evaluate new software tools and frameworks for readiness, compatibility, and performance
• Write and maintain user documentation, examples, and best‑practice guides
• Deliver technical workshops or tutorials for domain scientists adopting advanced computing tools
• Contribute technical sections to grant proposals, publications, white papers, and presentations as needed
• Support students and junior researchers through code reviews, office hours, and one‑on‑one consultations
• Participate in pilot projects and cross‑institutional knowledge‑sharing initiatives

Job description

Computational Scientist, 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).

Position Summary

The 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 Responsibilities

• Work with researchers to build, debug, and tune computational workflows across HPC and AI systems
• Port, profile, and optimize GPU-accelerated applications, parallel codes, and distributed machine learning training pipelines
• Build scalable, reproducible workflows using tools such as Slurm, Dask, Apptainer, Snakemake, or Nextflow
• Contribute hands‑on to the technical components of research projects across partner institutions
• Perform data wrangling, model training, and performance benchmarking in collaboration with faculty
• Package research code into reusable software modules, containers, and scalable workflows
• Serve as a go‑to technical resource for AI/ML tools, GPU optimization, and data‑intensive computing
• Troubleshoot user jobs and work with system administrators to surface user needs and platform issues
• Test and evaluate new software tools and frameworks for readiness, compatibility, and performance
• Write and maintain user documentation, examples, and best‑practice guides
• Deliver technical workshops or tutorials for domain scientists adopting advanced computing tools
• Contribute technical sections to grant proposals, publications, white papers, and presentations as needed
• Support students and junior researchers through code reviews, office hours, and one‑on‑one consultations
• Participate in pilot projects and cross‑institutional knowledge‑sharing initiatives

Minimum Qualifications

• Ph.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 systems
• Strong 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 environment

Preferred Qualifications

• Hands‑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 clusters
• Experience 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 computing
• Contributions to open‑source scientific software projects or community research tools