Research Programmer

Position Details

Position Information
Recruitment/Posting Title
Research Programmer

Department
Quantitative Biomedicine Inst

Salary Details
$120,000 - $130,000

Offer Information
The final salary offer may be determined by several factors, including, but not limited to, the candidate's qualifications, experience, and expertise, and availability of department or grant funds to support the position. We also take into consideration market benchmarks, if and when appropriate, and internal equity to ensure fair compensation relative to the university's broader compensation structure. We are committed to offering competitive and flexible compensation packages to attract and retain top talent.

Benefits
Rutgers provides a comprehensive benefits package to eligible employees. The specific benefits vary based on the position and may include:

• Medical, prescription drug, and dental coverage
• Paid vacation, holidays, and various leave programs
• Competitive retirement benefits, including defined contribution plans and voluntary tax-deferred savings options
• Employee and dependent educational benefits (when applicable)
• Life insurance coverage
• Employee discount programs

Posting Summary
The Rutgers Artificial Intelligence and Data Science (RAD) Collaboratory is seeking one or more Research Programmers to support leveraging modern Machine Learning (ML) and Deep Learning (DL) techniques by Rutgers faculty, postdoctoral fellows, and students. The ideal candidate will have a strong ML/DL and cyberinfrastructure (CI) background and a willingness to contribute to interdisciplinary research across diverse basic and applied science and engineering domains.

Responsibilities will include:
* Design, develop, and deploy ML/DL algorithms for domain science and engineering fields
* Support RAD Collaboratory research on national cyberinfrastructure (e.g., ACCESS, NAIRR, and DOE supercomputers) or cloud environments (e.g., AWS, GCP, and Azure)
* Support application of ML/DL/CI techniques across topics and domains
* Deliver training on ML/DL/CI techniques and best practices to a broad range of researchers
* Stay at the forefront of new ML/DL techniques and ML/DL systems that support science and engineering research
* Co-author peer-reviewed interdisciplinary research publications
* Contribute to funding applications from external sources (e.g., NSF, NIH)

Position Status
Full Time

Posting Number
25FA0745

Posting Open Date
08/01/2025

Posting Close Date

Qualifications
Minimum Education and Experience
Ph.D. in computer science, engineering, or other related research fields with a strong background in applied ML/DL in interdisciplinary research.

Experience working with ML/DL platforms and algorithms.

Track record of working with domain experts, researchers, and stakeholders to support diverse science and engineering applications.

Certifications/Licenses

Required Knowledge, Skills, and Abilities
* Experience with DL frameworks such as PyTorch, DeepSpeed, Accelerate, or Megatron-LM * Experience with large language model (LLM) techniques such as supervised fine-tuning, retrieval augmented generation, and in-context learning
* Advanced Statistical Analysis: Proficiency in advanced statistical techniques and probability theory
* GPU Programming: Experience with GPU programming and optimization for ML models, utilizing frameworks, like CUDA or OpenCL * Experience with applied computer vision, such as convolutional neural networks and vision transformers is preferred * Experience in deploying open-source and open-data DL projects at scale and job management with SLURM or PBS is preferred * Knowledge of software engineering and MLOps (e.g., CI/CD workflow) is preferred * Familiarity with scientific or ML workflows is preferred * Training or tutorial experience for domain scientists * Ability to learn and adapt to new technologies * Excellent writing and verbal communication skills

Preferred Qualifications

Equipment Utilized

Physical Demands and Work Environment
Individual will work onsite at RCSB PDB located at Rutgers Busch Science Campus (Piscataway, NJ)

Overview
The RAD Collaboratory was recently launched by the Office of the Rutgers New Brunswick Chancellor as a Chancellor-reporting Signature Initiative (CSI) that aligns with Rutgers-New Brunswick Academic Master Plan. This initiative serves as a hub for data science, artificial intelligence, student programming, and community engagement.

Statement

Posting Details
Special Instructions to Applicants

Quick Link to Posting

Campus
Rutgers University-New Brunswick

Home Location Campus
Busch (RU-New Brunswick)

City
Piscataway

State
NJ

Location Details

Pre-employment Screenings
All offers of employment are contingent upon successful completion of all pre-employment screenings.

Immunization Requirements

Under Policy 100.3.1 Immunization Policy for Covered Individuals, if employment will commence during Flu Season, Rutgers University may require certain prospective employees to provide proof that they are vaccinated against Seasonal Influenza for the current Flu Season, unless the University has granted the individual a medical or religious exemption. Additional infection control and safety policies may apply. Prospective employees should speak with their hiring manager to determine which policies apply to the role or position for which they are applying. Failure to provide proof of vaccination for any required vaccines or obtain a medical or religious exemption from the University will result in rescission of a candidate's offer of employment or disciplinary action up to and including termination.

Equal Employment Opportunity Statement
It is university policy to provide equal employment opportunity to all its employees and applicants for employment regardless of their race, creed, color, national origin, age, ancestry, nationality, marital or domestic partnership or civil union status, sex, pregnancy, gender identity or expression, disability status, liability for military service, protected veteran status, affectional or sexual orientation, atypical cellular or blood trait, genetic information (including the refusal to submit to genetic testing), or any other category protected by law. As an institution, we encourage all qualified applicants to apply. For additional information please see the Non-Discrimination Statement at the following web address: