Organization Overview: The Bioinformatics Resource Center (BRC) provides bioinformatics analysis, experimental design consultation, software infrastructure, and training to the scientific community at The Rockefeller University. With a specific expertise in the processing and analysis of high-throughput genomic sequencing data, and in collaboration with both wet and dry lab biologists, the BRC aims to support and accelerate the diverse and cutting-edge research conducted at the university through the creation of analytical pipelines; the analysis of biological data via direct collaborations; and the training of Rockefeller's faculty, students, and scientific staff. Overview: Responsibilities:

• Design, implement, and maintain AI/ML pipelines for genomics and multi-omics data (RNA-seq, ATAC-seq, ChIP-seq, functional genomics)
• Apply and adapt machine learning and deep learning models (e.g., convolutional and transformer-based architectures) to biological questions in collaboration with investigators.
• Develop interpretable models and attribution analyses (e.g., motif discovery, perturbation and variant-effect analyses) to support biological insight.
• Build, document, and containerize reproducible workflows suitable for shared HPC/GPU environments.
• Provide consultative support and training to researchers using BRC AI/ML tools and pipelines.
• Performs related duties & responsibilities as assigned/requested.

Qualifications:

• Master’s degree in Bioinformatics, Computational Biology, Computer Science, or a related field
• A minimum of five years of relevant experience applying computational or machine learning methods to biological data.

• PhD in Bioinformatics, Computational Biology, Computer Science, or a related field
• Experience applying deep learning or foundation-model approaches to sequence-based or multimodal genomics data.
• Experience in a core facility or highly collaborative research environment.
• Familiarity with model interpretability for biological insight (motif analysis, attribution methods, TF/RBP modeling).
• Experience working with HPC/GPU resources and job schedulers (e.g., Slurm) and/or cloud-based deployments.
• Track record of contributing to peer-reviewed publications as a computational specialist.
• Interest in mentoring and training researchers in applied AI/ML methods.

Compensation Range: Min: USD $90,000.00/Yr. Compensation Range: Max: USD $115,000.00/Yr.