Full-Time

Staff Scientist

Black Canyon Consulting

Black Canyon Consulting

Compensation Overview

$110k - $140k/yr

Bethesda, MD, USA

In Person

PhD

Category
AI & Machine Learning (2)
,
Required Skills
Microsoft Azure
Python
TensorFlow
Neural Networks
PyTorch
Docker
AWS
Linux/Unix
Google Cloud Platform

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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)
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
  • 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
Desired 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
Black Canyon Consulting

Black Canyon Consulting

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