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AI Drug Repurposing Candidate Ranker

Wyatt Tanaka

Full-time · Job · New_York

About the role

Developing a new drug costs $2.6 billion and takes 12 years. Repurposing an existing approved drug for a new indication costs a fraction and takes 3-5 years — if you can find the right candidates. Our platform screens 12,000 approved drugs against 8,000 disease targets using knowledge graph embeddings, molecular docking simulations, and clinical evidence mining to rank repurposing opportunities. The ranking model must balance multiple evidence types that disagree: a drug might score high on molecular similarity to known treatments but have weak clinical evidence, or strong epidemiological signal but poor structural fit. You'll build the multi-criteria ranking system that fuses these heterogeneous evidence sources into a single prioritized list that our pharmaceutical partners use to select candidates for clinical validation. The drug discovery team is six researchers and three engineers. You'll be the senior ML engineer who bridges computational biology and production engineering — building models that scientists trust and systems that scale to the full drug-disease matrix.

Responsibilities

  • Build multi-criteria ranking models fusing molecular, clinical, and epidemiological evidence
  • Design knowledge graph embeddings for drug-target interaction prediction
  • Handle evidence fusion across heterogeneous data types and confidence levels
  • Validate ranking against known repurposing successes and ongoing trials
  • Scale the ranking system to the full 12K drug × 8K disease matrix
  • Present findings to pharmaceutical partner scientific review boards

Requirements

  • 6+ years in ML with biomedical or drug discovery experience
  • Experience with knowledge graph embeddings and link prediction
  • Strong understanding of multi-criteria decision making and ranking
  • Python, PyTorch, and scientific computing skills
  • Familiarity with biomedical ontologies and literature mining

Benefits

  • Fully remote with quarterly team meetings
  • Top-of-market compensation plus equity
  • Comprehensive health coverage
  • $5,000 annual conference budget
  • Work that accelerates treatment discovery for patients

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Categories

AI & Machine Learning Engineers data science & analytics

Skills

python pytorch scikit-learn 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 scikit-learn sql docker statistics

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