• • Develop and deploy predictive models and risk stratification frameworks linking imaging, lab, and genomic biomarkers.
  • Implement pattern recognition and feature correlation pipelines to detect early biological changes across systems (e.g., neuro–metabolic, musculoskeletal–metabolic).
  • Translate scientific hypotheses into computational models that can be tested and validated using Q Bio’s internal datasets.
  • Integrate models into the Gemini and Constellation platforms for visualization and clinical interpretation.
  • Collaborate with engineers on scalable, production-ready codebases and model deployment pipelines.
  • Leverage Q Bio’s diverse datasets and external cohorts for model validation.
  • Partner with clinical and regulatory stakeholders to ensure model robustness and alignment with submission requirements.

What You Will Bring:

• • MS or PhD in Biomedical Engineering, Computational Biology, Data Science, or related quantitative discipline.
  • 6+ years of experience building and validating machine learning or predictive models in biomedical or imaging domains.
  • Advanced proficiency in Python, scientific computing, and ML frameworks (scikit-learn, PyTorch, TensorFlow).
  • Deep understanding of statistical learning, data normalization, and model interpretability in heterogeneous biomedical datasets.
  • Track record of delivering production-quality analytical models or pipelines (not just prototypes).
  • Excellent communication skills and the ability to collaborate with cross-functional teams (engineering, clinical, product).
  • Preferred
  • Experience integrating quantitative imaging data (MRI, qMRI, CT) with biochemical, genomic, or clinical biomarkers.
  • Familiarity with biological pathway modeling or multi-omics integration.
  • Exposure to regulated environments (FDA submissions, IRB-approved studies, clinical validation).
  • Demonstrated ability to move from concept to deployment in small, fast-paced teams

$175,000 - $215,000 a year

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.