AI Research Scientist - AI Biological Design

About the role

We are searching for a curious and motivated AI Research Scientist to invent and advance machine learning methods for biological discovery at scale. Rather than primarily applying established techniques, this role focuses on methodological innovation – designing, testing, and refining new AI/ML models that operate across diverse and large-scale biological data, and translating complex scientific questions into computational frameworks. Working closely with scientists and engineers, the AI Research Scientist evaluates model performance, limitations, and scientific relevance, and contributes to the broader scientific community through publications, benchmarks, and reusable methods. The role operates in high-ambiguity, research-driven problem spaces where outcomes are uncertain, and is accountable for scientific contribution and methodological advancement rather than for production systems or analytics delivery.

Responsibilities

• Develop, train, and evaluate large-scale AI/ML models across diverse biological data types, with attention to scientific relevance and rigor
• Investigate new modeling paradigms – such as representation learning, generative models, foundation models, and multimodal learning – to address open biological research questions
• Translate complex scientific questions into well-posed computational frameworks, experiments, and benchmarks
• Assess model behavior, limitations, and interpretability in scientific contexts, and communicate findings clearly to scientific and technical collaborators
• Design and run experiments at scale, including benchmarking and model analysis, using modern training and evaluation environments
• Build reusable modeling frameworks and reference implementations that accelerate research across teams
• Document methods and support reproducibility and open science practices, including code, data, and benchmark release where appropriate
• Contribute to the broader scientific community through publications, collaborations, conference participation, and methodological work
• Participate in institute-wide initiatives, workshops, and seminars to promote scientific and engineering excellence through technical leadership and cross-disciplinary collaboration

Key Contributions

• Novel AI/ML models, algorithms, or representations developed for scientific problems
• Research publications, benchmarks, and methodological contributions
• Reusable modeling frameworks and reference implementations
• Scientific insights enabled by advanced AI-driven analysis

Requirements

• PhD in Computer Science, Applied Mathematics, Statistics, Computational Biology, or a related field; or equivalent combination of degree and experience
• Demonstrated experience developing and evaluating novel AI/ML approaches for complex, large-scale datasets
• Proficiency in Python and modern deep learning frameworks (e.g., PyTorch, JAX)
• Strong foundation in machine learning, deep learning, and scientific computing, including experimentation, benchmarking, and model analysis

Skills

• Research experience applying machine learning to biological, genomic, or other life-science data (e.g., foundation models, taxonomy or sequence classification, multimodal or signal data)
• Experience building and deploying scalable research tooling, pipelines, or command-line applications that empower scientific R&D
• Familiarity with large-scale training and evaluation environments and with scientific computing and modeling libraries
• A record of methodological contribution – publications, benchmarks, open-source releases, or other externally visible scientific work
• Excellent written and verbal communication skills, with the ability to collaborate effectively in a multidisciplinary team environment
• Demonstrated ability to work independently and manage multiple research efforts simultaneously while meeting milestones

This role focuses on inventing and advancing AI/ML methods, not primarily applying established techniques. It operates in high-ambiguity, research-driven problem spaces where outcomes are uncertain and is accountable for scientific contribution and methodological advancement, rather than production systems or analytics delivery. It is distinct from Data Scientist roles, which emphasize applied modeling and insight generation, and distinct from Scientific Data Engineer roles, which emphasize data pipelines, infrastructure, and ML operations.

This role requires onsite work at 700 Dexter Ave N. in Washington State and is expected to work onsite for the majority of working hours. Fine motor movements in fingers/hands to operate computers and other office equipment are required. Attendance and participation in national and international conferences as appropriate.

This opportunity may provide work visa sponsorship and offers relocation assistance.

Pay

$146,600 - $183,250. Final salary depends on required education for the role, experience, and level of skills relevant to the role, along with work location, where applicable.

Benefits

• Medical, dental, vision, and basic life insurance
• 401k plan
• Paid time off as outlined in the Allen Institute’s Benefits Guide

Details on the Allen Institute’s benefits offering are located at https://alleninstitute.org/careers/benefits.