AI Skills for Genomics & Multi-Omics Jobs
Genomics and multi-omics roles apply machine learning to sequencing, single-cell, and spatial data at scale.
From 105 live Genomics & Multi-Omics roles, within our analysis of 494 AI/ML postings · updated October 1, 2026
What Genomics & Multi-Omics roles actually do
You apply ML to sequencing, single-cell, and spatial data: building pipelines, integrating multi-omics, and pulling biological signal out of large, noisy datasets. Much of the work is getting the data right so the modeling can be trusted.
Most-requested skills for Genomics & Multi-Omics roles
Share of the 105 live Genomics & Multi-Omics AI/ML postings mentioning each skill.
The core stack, explained
The tools that show up most in Genomics & Multi-Omics postings, and why they matter.
Who's hiring
Companies with the most open Genomics & Multi-Omics AI/ML roles right now.
Typical pay
Median-to-median disclosed salary band across these roles, where pay is posted.
What makes Genomics & Multi-Omics different
Here the data and statistics matter as much as the model: single-cell analysis, genomics and bioinformatics rank near the top, and solid statistics and reproducible pipelines are what separate a usable result from a nice demo. Deep learning is increasingly common, but reproducible analysis of messy biological data is the core skill.
How to break into Genomics & Multi-Omics
Show you can take real sequencing or single-cell data end to end: a reproducible pipeline plus a defensible analysis matters more here than a fancy model, because handling messy biological data is the hard part.
Frequently asked
What skills do genomics & multi-omics jobs require?
Across 105 live Genomics & Multi-Omics AI/ML roles, the most-requested skills are Python, Genomics, Bioinformatics, Single-cell, Foundation models, LLMs. Percentages for each are in the breakdown above.
How is Genomics & Multi-Omics different from general AI/ML in biotech?
Here the data and statistics matter as much as the model: single-cell analysis, genomics and bioinformatics rank near the top, and solid statistics and reproducible pipelines are what separate a usable result from a nice demo. Deep learning is increasingly common, but reproducible analysis of messy biological data is the core skill.
Do I need R or Python for genomics roles?
Both help. Python dominates ML, but R remains common for statistics and single-cell genomics, and many roles list both. Being strong in one and comfortable in the other is ideal.
Is deep learning necessary for genomics jobs?
Not always. Solid pipelines, statistics, and single-cell analysis carry many roles; deep learning is a growing plus, especially for sequence and expression models.
Find Genomics & Multi-Omics roles
Browse live genomics & multi-omics and related AI/ML roles at top biotech and pharma companies.
Browse all jobsPart of the AI/ML Skills in Biotech guide. Explore other sub-fields: Protein Design & Structure · AI Drug Discovery & Chemistry · Imaging & Pathology · Clinical & Translational · ML Engineering & Platform