AI Research Scientist - AI BioDesign

Allen Institute; Allen (TX); Hybrid; USD 147,000 - 183,000; Full time

Benefits offered by this job

Relocation assistance

Benefits package

Job summary

The Allen Institute in Washington State seeks an AI Research Scientist to invent and advance machine learning methods for biological discovery at scale. You will design, test, and refine new AI/ML models that operate across diverse and large-scale biological data, translating questions into computational frameworks.

You will work with scientists and engineers to evaluate model performance, communicate findings, and contribute to publications, benchmarks, and reusable methods in an open science

Qualifications

• 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

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

Skills

Python

ML research

Deep learning

Scientific computing

Experimentation

Education

PhD in CS/Applied Math/Stats/Computational Biology

Equivalent degree + experience

Tools

PyTorch

JAX

Job description

The Allen Institute accelerates science for a healthier world through large-scale research designed to answer some of the most complex questions in biology. Our multi-disciplinary teams generate foundational knowledge, tools, and data to understand how our brain, cells, and immune system work. We share our work openly so others can build on it, move faster, and ask bigger questions. We drive discovery forward and create new possibilities for improving human health.

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.

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.

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.

Essential Functions

• 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

Required Education and Experience

• 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

Preferred Education and Experience

• 0 – 3 years of postdoctoral experience developing and applying ML methods
• 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

What Distinguishes This Role

• Focuses on inventing and advancing AI/ML methods, not primarily applying established techniques
• Operates in high‑ambiguity, research‑driven problem spaces where outcomes are uncertain
• Accountable for scientific contribution and methodological advancement, rather than production systems or analytics delivery
• Distinct from Data Scientist roles, which emphasize applied modeling and insight generation
• Distinct from Scientific Data Engineer roles, which emphasize data pipelines, infrastructure, and ML operations

Physical Demands

• Fine motor movements in fingers/hands to operate computers and other office equipment

Position Type / Expected Hours of Work

• This role requires onsite work and is expected to work onsite for the majority of the working hours. We are a Washington State employer, and the primary work location for Allen Institute employees is 700 Dexter Ave N.; any remote work must be performed in Washington State

Travel

• Attendance and participation in national and international conferences as appropriate

Additional Comments

• Please note, this opportunity may provide work visa sponsorship
• Please note, this opportunity offers relocation assistance

Annualized Salary Range

• $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

• Employees (and their families) are eligible to enroll in benefits per eligibility rules outlined in the Allen Institute’s Benefits Guide. These benefits include medical, dental, vision, and basic life insurance. Employees are also eligible to enroll in the Allen Institute’s 401k plan. Paid time off is also available as outlined in the Allen Institute’s Benefits Guide. Details on the Allen Institute’s benefits offering are located at the following link to the Benefits Guide: https://alleninstitute.org/careers/benefits .

It is the policy of the Allen Institute to provide equal employment opportunity (EEO) to all persons regardless of age, color, national origin, citizenship status, physical or mental disability, race, religion, creed, gender, sex, sexual orientation, gender identity and/or expression, genetic information, marital status, status with regard to public assistance, veteran status, or any other characteristic protected by federal, state or local law. In addition, the Allen Institute will provide reasonable accommodations for qualified individuals with disabilities.

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