AI Research Scientist II — Multimodal Biology AI

Allen Institute; Seattle (WA); Hybrid; USD 168,000 - 210,000; Full time

Benefits offered by this job

Relocation assistance

Potential visa sponsorship

Job summary

The Allen Institute's Office of the CTO is seeking an independent AI Research Scientist II to build large AI/ML models for biology. You will work with multidisciplinary teams across Brain Science, Cell Science, Immunology, Neural Dynamics and SeaHub to represent, analyze and interpret multimodal data, aiming to advance understanding of health and disease.

Reporting to the Senior Director of AI Applications, you will develop SOTA methods, contribute to open science, and help establish scalable AI

Qualifications

• PhD in CS, applied math, computational biology, statistics or similar, or equivalent combination
• Minimum of 2 years postdoctoral/work experience
• Demonstrated ability to design and apply AI/ML models for large-scale biological data

Responsibilities

• Develop and deploy large-scale ML models to study complex biological systems
• Collaborate across teams to drive AI/ML initiatives
• Build scalable AI infrastructure and data pipelines
• Promote open science through publications and open-source code
• Participate in institute-wide workshops and knowledge sharing

Skills

Strong publication record

Data pipelines

Data visualization

Cross-disciplinary collaboration

Problem solving

Education

PhD in Computer Science, Applied Mathematics, Computational Biology, Statistics, Biostatistics or related field

Tools

PyTorch

Jax

Pyro

NumPy

Pandas

Job description

The Allen Institute's Office of the CTO is seeking an independent AI Research Scientist II to build large AI/ML models for biology. You will work with multidisciplinary teams across Brain Science, Cell Science, Immunology, Neural Dynamics and SeaHub to represent, analyze and interpret multimodal data, aiming to advance understanding of health and disease.

Reporting to the Senior Director of AI Applications, you will develop SOTA methods, contribute to open science, and help establish scalable AI