Lead ML Scientist for AI-Driven Drug Discovery

Engg; Somerville (MA); On-site; USD 132,000 - 209,000; Full time

Benefits offered by this job

Healthcare coverage

Annual incentive program

Retirement benefits

Job summary

Cellarity is seeking a Machine Learning Scientist to join its ML team and lead efforts in applying foundation models to enable its predictive drug discovery platform. The role involves hands-on modeling of high‑dimensional biological data and collaboration with biology, chemistry, and omics researchers.

Ideal candidates hold a PhD or equivalent and have a strong background in deep learning, generative AI, and single-cell RNA‑seq analyses (Perturb-seq).

Qualifications

• PhD or equivalent in CS/Computational Biology with ML focus.
• Strong DL and generative AI background.
• Experience with high-dimensional biological data (scRNA-seq, gene networks).
• Experience with Perturb-seq/CRISPR data analysis.
• Familiar with single-cell foundation models (Geneformer, scGPT, scFoundation).
• Excellent collaboration across biology, chemistry, and omics teams.

Responsibilities

• Model development: fine-tune foundation models on scRNA-seq data and other modalities.
• Build multi-modal perturbation models (small molecules, CRISPR, cytokines) incl. Perturb-seq data.
• Develop interpretability methods to infer gene networks and regulatory mechanisms.
• Deploy inference on cloud platforms and manage experiments.
• Establish benchmarking frameworks for context generalization.
• Collaborate with researchers across biology, chemistry, and omics.

Skills

Deep learning

Computational biology

Single-cell RNA-seq

Perturb-seq data

Foundation models

Generative AI

Cloud computing (AWS/GCP)

MLOps

Biological data analysis

Education

PhD in Computer Science/Computational Biology

Master's degree +3 years ML research

Bachelor's degree +6 years ML research

Tools

Geneformer

scGPT

scFoundation

Perturb-seq data analysis

CRISPR data analysis

AWS

GCP

Job description

Cellarity is seeking a Machine Learning Scientist to join its ML team and lead efforts in applying foundation models to enable its predictive drug discovery platform. The role involves hands-on modeling of high‑dimensional biological data and collaboration with biology, chemistry, and omics researchers.

Ideal candidates hold a PhD or equivalent and have a strong background in deep learning, generative AI, and single-cell RNA‑seq analyses (Perturb-seq).