Principal Scientist - Computational Biologist and AI/ML Researcher

The mission of the Allen Institute is to unlock the complexities of bioscience and advance our knowledge to improve human health.

Using an open science, multi-scale, team-oriented approach, the Allen Institute focuses on accelerating foundational research, developing standards and models, and cultivating new ideas to make a broad, transformational impact on science.

The mission of the Office of the Chief Technology Officer (CTO) is not only to provide state of the art engineering infrastructure to the Allen Institute as a whole, but to also address high-risk, high-reward questions in biology through AI.

We are seeking an independent and motivated researcher in the Office of the CTO to build large AI / ML models for Biology.

Specifically, you will develop and apply SOTA AI / ML methods for the representation, analysis and interpretation of multimodal data to understand biological systems in health and disease.

This role works with diverse biological datatypes, including multi-omics data, microscopy data, in vivo and behavioral imaging data, electrophysiological data, and clinical data.

The role is also expected to work collaboratively across all of the Allen Institutes including Brain Science, Cell Science, Immunology, Neural Dynamics, and the Seattle Hub for Synthetic Biology.

AI / ML focus areas could include multimodal foundation models, diffusion models, causal representation learning, and beyond.

The Allen Institute believes that team science significantly benefits from the participation of diverse voices, experiences and backgrounds.

High-quality science can only be produced when it includes different perspectives. We are committed to increasing diversity across every team and encourage people from all backgrounds to apply for this role.

Essential Functions

• Develop and deploy ML models that deepen our understanding of complex biological systems in health and disease
• Promote open science through publishing papers and open-source code
• Collaborate with teams of scientists, computational biologists, and software engineers within the Allen Institute and external partners
• Advance community standards for scalability in developing, disseminating, and evaluating AI / ML / computational methods for scientific problems
• Lead the development of state-of-the-art engineering infrastructure at the Allen Institute to support AI / ML research and applications
• Stay up-to-date with the latest advancements in AI / ML and their potential applications in biological research, ensuring the Allen Institute remains at the forefront of this field
• Foster a collaborative and inclusive work environment that values diversity and encourages participation from team members with different voices, experiences, and backgrounds
• Mentor and guide junior researchers, interns, or students working on AI / ML projects related to biological research
• Participate in institute-wide initiatives, workshops, and seminars to promote cross-disciplinary collaboration and knowledge sharing

Note : Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

This description reflects management’s assignment of essential functions; it does not proscribe or restrict the tasks that may be assigned.

Required Education and Experience :

• PhD in Computer Science, Applied Mathematics, Computational Biology, Statistics, Biostatistics or similar field; or equivalent combination of degree and experience
• 11 years of equivalent experience
• Demonstrated ability to design, implement and apply AI / ML models for the analysis of large-scale biological data

Preferred Education and Experience :

• 15+ years of experience developing and applying ML methods
• Strong publication record of innovative scientific accomplishments (both individual and team)
• Expertise in Python-based ML libraries and frameworks such as PyTorch, Jax, Pyro, NumPy, and Pandas
• Solid understanding of statistical analysis, data preprocessing, feature selection, and model evaluation techniques
• Experience building data pipelines to make biological data ML-ready, pipeline for model training and evaluation
• Knowledge of data preprocessing, normalization, and integration techniques specific to biological and clinical datasets
• Experience with distributed computing for ML models, (e.g. distributing load across multiple nodes, Ray, HPC, Distributed PyTorch, etc.)
• Experience with data visualization and presentation of complex biological findings to both technical and non-technical audiences
• Strong problem-solving skills and ability to develop innovative computational approaches to address complex biological questions
• Proven ability to work independently and manage multiple projects simultaneously while meeting deadlines
• Excellent written and verbal communication skills, with the ability to collaborate effectively in a multidisciplinary team environment

Physical Demands

Fine motor movements in fingers / hands to operate computers and other office equipment; repetitive motion with lab equipment

Position Type / Expected Hours of Work

This role is currently able to work both remotely and onsite in a hybrid work environment. We are a Washington State employer, and the primary work location for all Allen Institute employees is 615 Westlake Ave N.

any remote work must be performed in Washington State.

Travel

• Attendance and participation in national and international conferences
30+ days ago