# Young Investigator, Open Language Models for Biology

**Company:** [Ai2](https://hotfix.jobs/companies/ai2)
**Location:** Seattle, WA
**Role:** AI Research
**Salary:** $160k – $160k/yr
**Experience:** 0+ years
**Skills:** Python, PyTorch, Transformers, scrna-seq, snrna-seq, scanpy, anndata, scvi, multimodal models, instruction tuning, scgpt, geneformer, cellwhisperer, c2s-scale
**Posted:** 2026-07-28

> Postdoctoral Young Investigator role developing open multimodal language models (CellOLMo) that integrate single-cell transcriptomics with biological text and knowledge to study Alzheimer's disease and neurodegeneration. Requires PhD in ML, computational biology or related quantitative field plus hands-on transformer model experience.

## Job Description

## Your Next Challenge

Develop a multimodal language model that combines single-cell gene-expression data with text, using an open model such as OLMo.
Reproduce and evaluate relevant approaches, including scGPT, Geneformer, CellWhisperer, and C2S-Scale.
Adapt and improve these approaches for brain and neurodegeneration data.
Design controlled experiments to test whether language grounding improves performance over cell-only and text-free baselines.
Develop representations at the brain-region and donor levels that incorporate pathology and disease progression.
Evaluate models on held-out donors using measures such as cell-type and marker recovery, agreement with known patterns of regional vulnerability, and expert review of plain-language answers.
Work with Allen Institute scientists to interpret results and identify predictions suitable for experimental follow-up.
Release data, code, and model weights, and prepare the results for publication.

## What You’ll Need

- A completed PhD, or a PhD expected within one year, in machine learning, computational biology, computational neuroscience, physics, or a related quantitative field.
- Hands-on experience training or fine-tuning transformer-based language models.
- Strong Python and PyTorch skills.
- Experience writing reliable, reproducible research code.
- The ability to carry out independent research on open-ended problems.
- The ability to communicate clearly with both machine-learning researchers and biological scientists.

We also look favorably upon experience in one or more of the following areas:
- Single-cell genomics, including scRNA-seq or snRNA-seq.
- Tools such as scanpy, AnnData, or the scVI ecosystem.
- Multimodal models, instruction tuning, or language-model post-training.
- Neurodegeneration, brain atlases, or spatial transcriptomics.
- Public releases of research code, datasets, or model weights.

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