AI Research Scientist

AI Research Scientist

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.

Our scientific teams address high-risk, high-reward questions in biology through AI – inventing computational methods and large-scale AI/ML models that turn complex biological data into scientific insight. This work spans collaborations across Allen Institute Accelerators, including Brain Science, Cell Science, Immunology, Neural Dynamics, and the Seattle Hub for Synthetic Biology, as well as external academic and institutional partners.

We are seeking a curious and motivated AI Research Scientist to invent and advance machine learning methods for biological discovery her 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.

At the Allen Institute, we believe that science is for everyone – and should be open to everyone. We are dedicated to combating biases and reducing barriers to STEM careers more broadly. We strive to make the Allen Institute a place where everyone feels like they belong and are empowered to do their best work in a supportive environment.

We are an equal-opportunity employer and strongly encourage people from all backgrounds to apply for our open positions.

• 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

• 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

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

• PhD in Computer Science, Applied Mathematics, Statistics, Computational Biology, or a related field; or…