Staff Scientist

Benefits

Parental leave

Not verified

Non-birth-parent leave

Not verified

Family-building benefits

• Fertility benefits: Not verified
• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

Not verified

Relocation assistance

Not verified

Childcare support

Not verified

Learning budget

Offered From the posting source checked Jun 20, 2026

Verification

Not verified checked Jun 13, 2026

Salary

$110K-$140K From the posting source checked Jun 20, 2026

401(k) match

Reported from DOL Form 5500 industry filing (not employer-specific)

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

U.S. benchmark only; posted salary is not compared across countries or currencies.

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Data From the posting source checked Jun 20, 2026

Seniority

Staff Plus From the posting source checked Jun 20, 2026

Schedule

Shift type

Not verified

Weekend work

Not verified

Company

Company stage

Public-company From the posting source checked Jun 20, 2026

Equity

Offered Verified - SEC 10-K source checked Jun 20, 2026

Application

Cover letter

Not verified

Assessment

Not verified

Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

Staff Scientist Bethesda MD 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)

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