Multimodal Deep Learning Scientist (PhD)

Tailored Management; South San Francisco (CA); On-site; USD 51,000 - 73,000; Full time

Benefits offered by this job

Weekly Pay

Medical Insurance

Dental Insurance

Vision Insurance

Job summary

Tailored Management seeks a Computational Scientist to support the Regev Lab in analyzing large-scale multimodal perturbation datasets. You will develop, train, and evaluate deep learning models to understand how dataset components affect performance and learning outcomes.

You will work with large-scale experimental datasets, compare model outputs to experiments, and generate insights to drive improvements.

Qualifications

• PhD in a computational discipline or equivalent research experience.
• Preferred: Computational Biology, Computational Sciences, Statistics.

Responsibilities

• Train, develop, and evaluate deep learning models using large-scale multimodal perturbation datasets.
• Analyze how different subsets and components of datasets contribute to model learning and overall performance.
• Design and execute controlled training and testing experiments.
• Evaluate model predictions and outputs against experimental data.
• Perform statistical analysis to assess model performance and validate findings.
• Work with existing analysis codebases and cluster computing infrastructure.
• Maintain reproducible, well-documented, and structured analytical workflows.
• Collaborate with project lead and research team to deliver milestones.

Skills

PyTorch

Train/Test Split

Statistics

Python

Reproducible workflows

Education

PhD in computational discipline

Computational Biology

Computational Sciences

Statistics

Tools

NumPy

Scanpy

AnnData

SLURM HPC

Job description

Tailored Management seeks a Computational Scientist to support the Regev Lab in analyzing large-scale multimodal perturbation datasets. You will develop, train, and evaluate deep learning models to understand how dataset components affect performance and learning outcomes.

You will work with large-scale experimental datasets, compare model outputs to experiments, and generate insights to drive improvements.