AI Biomarker Discovery Platform Engineer

Axion Labs

Remote · $160k – $200k / year · Full-time · Posted 5 months ago

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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.

Fully remote.

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