ML/DL Research Programmer for Interdisciplinary Science

Rutgers University; Piscataway Township, Northern (NJ, KY); Hybrid; USD 120,000 - 130,000; Full time

Benefits offered by this job

Medical, prescription drug, and dental

Paid vacation and leave programs

Competitive retirement benefits

Job summary

Rutgers University’s RAD Collaboratory invites researchers to advance ML/DL methods across science and engineering domains. The role emphasizes algorithm design, deployment on national cyberinfrastructure and cloud platforms, and training researchers in ML/DL/CI best practices.

Strong collaboration with domain experts is essential to drive interdisciplinary results. Ideal candidates have a PhD with ML/DL expertise, experience with DL frameworks, and a track record of applying ML to domain

Qualifications

• Ph.D. in computer science, engineering, or related research field with a strong ML/DL background.
• Experience with ML/DL platforms and algorithms.
• Track record of working with domain experts, researchers, and stakeholders to support diverse science and engineering applications.

Responsibilities

• Design, develop, and deploy ML/DL algorithms for domain science and engineering fields.
• Support RAD Collaboratory research on national cyberinfrastructure or cloud environments (AWS, GCP, Azure).
• Support application of ML/DL/CI techniques across topics and domains.
• Deliver training on ML/DL/CI techniques to researchers.
• Stay at the forefront of new ML/DL techniques and systems.
• Co-author peer-reviewed interdisciplinary research publications.
• Contribute to funding applications from external sources (e.g., NSF, NIH).

Skills

PyTorch

DeepSpeed

Accelerate

Megatron-LM

LLM techniques

Statistical analysis

GPU programming

CI/CD familiarity

SLURM/PBS usage

Education

Ph.D. in CS/Engineering

ML/DL platforms experience

Tools

CUDA

OpenCL

SLURM

PBS

Job description

Rutgers University’s RAD Collaboratory invites researchers to advance ML/DL methods across science and engineering domains. The role emphasizes algorithm design, deployment on national cyberinfrastructure and cloud platforms, and training researchers in ML/DL/CI best practices.

Strong collaboration with domain experts is essential to drive interdisciplinary results. Ideal candidates have a PhD with ML/DL expertise, experience with DL frameworks, and a track record of applying ML to domain