Staff Scientist

<h2 class="iCIMS_InfoMsg iCIMS_InfoField_Job"><span style="font-size: large;">Overview</span></h2> <p>Black Canyon Consulting is seeking a&nbsp;<strong>Staff Scientist</strong> 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.</p> <p>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.</p> <h3><strong>Responsibilities</strong></h3> <ul> <li>Lead the design, development, and implementation of AI-driven models for gene regulation analysis</li> <li>Architect and scale a TREDNet-based framework for cloud-native execution</li> <li>Optimize models for distributed, multi-GPU training environments</li> <li>Integrate and analyze large-scale genomic and epigenomic datasets, including:</li> <ul> <li>ENCODE / modENCODE</li> <li>NIH Roadmap Epigenomics</li> <li>UCSC Genome Database</li> </ul> <li>Apply AI methodologies to functionally annotate repetitive genomic regions, including centromeres and telomeres</li> <li>Develop and maintain scalable, containerized pipelines using Docker and/or Singularity</li> <li>Implement MLOps best practices, including experiment tracking, model versioning, and reproducibility</li> <li>Deploy and manage workflows in cloud environments (AWS, GCP, or Azure)</li> <li>Collaborate with interdisciplinary teams across computational and life sciences domains</li> </ul> <h3><strong>Required Qualifications</strong></h3> <ul> <li>PhD in Computer Science, Computational Biology, Bioinformatics, or a related field</li> <li>Minimum of 5 years of experience developing and deploying machine learning or deep learning models</li> <li>Strong experience with cloud platforms (AWS, GCP, or Azure)</li> <li>Proficiency in deep learning frameworks (PyTorch preferred; TensorFlow or HuggingFace acceptable)</li> <li>Deep understanding of neural network architectures (CNNs, transformers, sequence models)</li> <li>Strong programming skills in Python and experience working in Linux-based environments</li> <li>Experience with MLOps practices, including experiment tracking and model versioning</li> <li>Experience building and deploying containerized workflows (Docker and/or Singularity)</li> <li>Experience with distributed training across GPUs or multi-node environments</li> <li>Strong knowledge of genomics, gene regulation, and epigenomics</li> <li>Experience working with large-scale biological datasets (e.g., ENCODE, Roadmap Epigenomics, UCSC Genome Browser)</li> <li>Familiarity with genomics data formats (FASTA, VCF, BAM/CRAM, BED)</li> </ul> <h3><strong>Preferred Qualifications</strong></h3> <ul> <li>Experience with Telomere-to-Telomere (T2T) genome assemblies</li> <li>Experience analyzing repetitive genomic regions (e.g., centromeres, telomeres)</li> <li>Background in regulatory, functional, or comparative genomics (e.g., human vs. mouse)</li> <li>Experience with hyperparameter tuning and large-scale model optimization</li> <li>Familiarity with genomic foundation models or sequence-based deep learning approaches</li> <li>Experience running ML workloads on GPU-enabled cloud or HPC environments</li> <li>Familiarity with workflow orchestration tools (e.g., Nextflow, Snakemake, Airflow)</li> <li>Experience transitioning research models into production-grade pipelines</li> <li>Familiarity with CI/CD and infrastructure-as-code tools (e.g., Terraform)</li> <li>Experience working in interdisciplinary teams</li> </ul> <h3><strong>Deliverables</strong></h3> <ul> <li>Develop a containerized (Docker/Singularity) TREDNet pipeline capable of scaling across multiple GPU nodes in a cloud environment</li> <li>Produce a comprehensive functional map of the T2T reference genome, identifying regulatory motifs in previously unresolved regions</li> <li>Develop comparative models between human and mouse cell lines to identify conserved regulatory mechanisms</li> </ul> <h2><strong>Benefits and Salary</strong></h2> <p>We attract the best people in the business with our competitive benefits package, including medical, dental, and vision coverage; a 401(k) plan with employer contribution; paid holidays, vacation, and tuition reimbursement.</p> <p>We offer a competitive salary commensurate with experience and location. The targeted range for this position is $110,000 - $140,000.</p> <p>If you enjoy being part of a high-performing, professional, technology-focused organization, please apply today!</p>