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AI Biomarker Discovery Platform Engineer

James Yamamoto

Full-time · Job · New_York

About the role

Drug discovery pipelines generate petabytes of multi-omics data — genomics, proteomics, metabolomics — but the biomarkers that predict therapeutic response hide in the noise. Our platform uses deep learning to find them, reducing the biomarker identification phase from 18 months to 6 weeks for our pharmaceutical partners. You'll own the model development pipeline: designing architectures that handle heterogeneous biological data types, building feature selection methods that produce clinically interpretable biomarker panels, and validating discoveries against independent patient cohorts. The models must satisfy both statistical rigor (p-values that survive Bonferroni correction) and clinical utility (biomarkers that lab technicians can actually measure). The computational biology team is seven engineers and three PhD scientists. You'll be the senior ML engineer responsible for architecture decisions, experiment design, and the production pipeline that delivers validated biomarker candidates to our pharma clients' wet labs.

Responsibilities

  • Design deep learning architectures for multi-omics biomarker discovery
  • Build feature selection pipelines producing clinically interpretable biomarker panels
  • Validate biomarker candidates against independent patient cohorts
  • Architect the production pipeline from raw data to validated candidates
  • Collaborate with bioinformatics scientists on domain-specific model constraints
  • Present findings to pharma partner scientific advisory boards

Requirements

  • 6+ years in ML engineering with biomedical or genomics data experience
  • Experience with high-dimensional feature selection under multiple testing correction
  • Strong PyTorch and distributed computing skills
  • Understanding of biological data types and their statistical properties
  • Track record of shipping ML systems in regulated environments

Benefits

  • Fully remote with quarterly team meetings
  • Top-of-market compensation plus equity
  • Comprehensive health coverage
  • $5,000 annual conference and learning budget
  • Direct impact on drug discovery timelines

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Categories

AI & Machine Learning Engineers data science & analytics

Skills

python pytorch spark sql docker

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$160k – $200k / year

AI Expertise

AI & Machine Learning Engineers data science & analytics

Skills

python pytorch spark sql docker aws

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