Overview Black Canyon Consulting is seeking a Staff Scientist to work with a Principal Investigatory in the National Institutes of Health at the National Library of Medicine to support the development of high-fidelity artificial intelligence models designed to decode the functional landscape of the human and mouse genomes. This effort will leverage Telomere-to-Telomere (T2T) reference assemblies to advance understanding of gene regulation, particularly within complex and repetitive genomic regions. This position requires a unique combination of computational genomics expertise, machine learning proficiency, and scalable software engineering capabilities to support large-scale data integration and model development.

Responsibilities

Lead the design, development, and implementation of AI-driven models for gene regulation analysis Architect and scale a TREDNet-based framework for cloud-native execution Optimize models for distributed, multi-GPU training environments Integrate and analyze large-scale genomic and epigenomic datasets, including: ENCODE / modENCODE NIH Roadmap Epigenomics UCSC Genome Database Apply AI methodologies to functionally annotate repetitive genomic regions, including centromeres and telomeres Develop and maintain scalable, containerized pipelines using Docker and/or Singularity Implement MLOps best practices, including experiment tracking, model versioning, and reproducibility Deploy and manage workflows in cloud environments (AWS, GCP, or Azure) Collaborate with interdisciplinary teams across computational and life sciences domains

Required Qualifications

PhD in Computer Science, Computational Biology, Bioinformatics, or a related field Minimum of 5 years of experience developing and deploying machine learning or deep learning models Strong experience with cloud platforms (AWS, GCP, or Azure) Proficiency in deep learning frameworks (PyTorch preferred; TensorFlow or HuggingFace acceptable) Deep understanding of neural network architectures (CNNs, transformers, sequence models) Strong programming skills in Python and experience working in Linux-based environments Experience with MLOps practices, including experiment tracking and model versioning Experience building and deploying containerized workflows (Docker and/or Singularity) Experience with distributed training across GPUs or multi-node environments Strong knowledge of genomics, gene regulation, and epigenomics Experience working with large-scale biological datasets (e.g., ENCODE, Roadmap Epigenomics, UCSC Genome Browser) Familiarity with genomics data formats (FASTA, VCF, BAM/CRAM, BED)

Preferred Qualifications

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Benefits

and Salary We attract the best people in the business with our competitive

package, including medical, dental, and vision coverage; a 401(k) plan with employer contribution; paid holidays, vacation, and tuition reimbursement.