# Staff Scientist at BCC-NIH Staff Scientist at BCC-NIH in Bethesda MD. Remote, paying $110k–$140k. Posted March 30, 2026. - Company: BCC-NIH - Location: Bethesda MD - Workplace: Remote - Pay: $110k–$140k - Experience: 5+ years - Posted: 2026-03-30 - Apply: https://job-boards.greenhouse.io/bcc/jobs/7684124003 - Source: https://jobmagic.ai/jobs/staff-scientist-bcc-nih-qe38x2 ## Requirements - 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) ## Description ### 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** - 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 ### **Deliverables** - 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 ### **Benefits and Salary** 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. We offer a competitive salary commensurate with experience and location. The targeted range for this position is $110,000 - $140,000. If you enjoy being part of a high-performing, professional, technology-focused organization, please apply today!