AI Biologist

Compensation: $120k – $180k • No equity

About the job
Bioinformatics Engineer/AI Biologist - Biosurveillance

As molecular data generation and frontier model intelligence grows, new approaches to data analysis are needed across the biotech industry. We are building intelligent, high-performance agents for biological data analysis, empowering over 5,000 scientists across 150+ R&D labs to handle data from instrument-to-insights.

We're seeking a Bioinformatics Engineer to join our Biosurveillance team, working at the frontier of what artificial intelligence can achieve in biology.

About The Role

You will contribute to our technical approach to teaching agents how to understand and handle complex infectious disease surveillance and molecular epidemiology data, working across end-to-end analysis workflows from sequencing outputs to real scientific decisions.

You will work as part of a team of software engineers and biologists building datasets to teach these agents to reason better across:

-Clinical or environmental metagenomics, including sequencing quality control, taxonomic classification, contamination assessment, pathogen discovery, and metagenomic assembly.
-Viral genome reconstruction, annotation, consensus generation, lineage or subtype assignment, and sequencing quality assessment.
-Variant calling, within-host diversity, mixed infections, haplotype reconstruction, recombination, and reassortment.
-Phylogenetics, phylodynamics, viral evolution, genomic epidemiology, transmission analysis, and outbreak reconstruction.
-Wastewater, air, or environmental surveillance, including mixture analysis, abundance estimation, and integration with clinical surveillance.
-Molecular assay design, including qPCR primers and probes, tiled amplicon schemes, inclusivity and exclusivity analysis, and assay-dropout monitoring.
-Host-response analysis using bulk RNA-seq, single-cell RNA-seq, spatial data, immune-receptor sequencing, or other multiomic measurements.
-Antigenic evolution, immune escape, vaccine-strain selection, antiviral resistance, or viral protein analysis.
-Infectious disease modeling, forecasting, population surveillance, and integration of genomic, epidemiological, and clinical data.
-Public-health bioinformatics, reproducible scientific software, data standards, uncertainty assessment, and communication of results to scientific decision-makers.

Experience across every area is not required. We value candidates who have developed depth in one area and can reason carefully across adjacent disciplines.

Requirements