Staff Scientist

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.

• →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

• 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)

• Experience with Telomere-to-Telomere (T2T) genome assemblies
• Experience analyzing repetitive genomic regions (e.g., centromeres, telomeres)
• Background in regulatory, functional, or comparative genomics (e.g., human vs. mouse)
• Experience with hyperparameter tuning and large-scale model optimization
• Familiarity with genomic foundation models or sequence-based deep learning approaches
• Experience running ML workloads on GPU-enabled cloud or HPC environments
• Familiarity with workflow orchestration tools (e.g., Nextflow, Snakemake, Airflow)
• Experience transitioning research models into production-grade pipelines
• Familiarity with CI/CD and infrastructure-as-code tools (e.g., Terraform)
• Experience working in interdisciplinary teams

• Develop a containerized (Docker/Singularity) TREDNet pipeline capable of scaling across multiple GPU nodes in a cloud environment
• Produce a comprehensive functional map of the T2T reference genome, identifying regulatory motifs in previously unresolved regions
• Develop comparative models between human and mouse cell lines to identify conserved regulatory mechanisms