Intern - Machine Learning for Neuroscience

Allen Institute; Seattle (WA); Hybrid; USD 43,000 - 62,000; Part time

Job summary

Allen Institute invites a Research Intern – Machine Learning for Neuroscience to work on large-scale neural and behavioral datasets. The one-year, part-time position blends machine learning with neuroscience and is mentored directly.

The intern will develop models to understand coordination across neural populations and will have potential conference or manuscript contributions. Starting late fall 2026, the role supports a hybrid work setup in Washington state, with on-site presence required at

Qualifications

• Currently enrolled in a master’s or PhD program in a quantitative field.
• Completed coursework in programming, linear algebra, probability and statistics, and ML or signal processing.
• Demonstrated experience with scientific computing in Python (numpy, scipy, pandas).
• Available to start in late October / early November 2026 and able to commit to the position for one year.
• Able to work onsite at least one day per week.

Responsibilities

• Characterize coordinated neural population activity to encode forelimb force.
• Implement and evaluate ML and latent variable models for shared vs population-specific covariance.
• Relate neural activity to behavior using regression and classification with cross-validation.
• Extend analysis to EMG and video data; study learning across days.
• Produce documented Python analysis code for the team; version-controlled.
• Communicate results to scientists and contribute to posters and manuscripts.

Skills

Strong written communication

Verbal communication

Independent work

Collaborative work

Education

Master’s or PhD in ECE / CS / quantitative field

Tools

Python

NumPy

SciPy

Pandas

scikit-learn

PyTorch

DeepLabCut

SLEAP

Lightning Pose

Job description

Research Intern – Machine Learning for Neuroscience

12 Month Duration - starting late fall 2026

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.

The goal of the Neural Dynamics accelerator is to understand how the brain generates flexible behavior. We aim to uncover the algorithms—implemented by dynamics in brain-wide neural circuits—that allow animals to build internal models, process information, and choose actions.

We are searching for a graduate student intern who will analyze large-scale neural and behavioral recordings to uncover how populations of neurons coordinate to control movement.

This is a one-year, part-time research position at the intersection of machine learning and neuroscience. The intern will work with a rich existing dataset: two intermingled but genetically distinct populations of neurons in the striatum, recorded simultaneously, with individual cells tracked stably across many days, alongside a continuously measured motor output and synchronized muscle activity and video. The central question is a latent-variable modeling problem — how do the two populations jointly encode the motor output, and how much of their activity is shared between them versus private to each? Understanding how these circuits specify movement is also foundational for closed-loop brain-machine interfaces that aim to restore movement after injury or disease, an active area of work in the team. No prior neuroscience background is required; the intern will be mentored directly. The position is a one-year commitment so that the intern has time to carry a project through to a scientific result, and strong work may contribute to a conference presentation or publication. Students at the University of Washington may be able to arrange academic credit for this work, including toward a master’s thesis, in coordination with their degree program and faculty advisor.

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 also believe that science is better when it includes different perspectives and voices. 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

• Characterize how coordinated activity across two simultaneously recorded neural populations encodes forelimb force
• Implement, fit, and evaluate machine learning and latent variable models that partition shared versus population-specific covariance, and benchmark them against recently published alternatives
• Relate neural population activity to behavioral variables using regression and classification methods, with appropriate cross-validation and controls
• Extend the analysis to electromyography (EMG) and synchronized behavioral video, and to how neural coding and behavior change across days of learning
• Produce documented, version-controlled Python analysis code that other team members can build on
• Communicate results to the mentoring scientists and the wider team, and contribute figures and methods text toward posters and manuscripts

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.

Educational Objectives

• Hands‑on experience applying machine learning and signal processing methods to large-scale neural and behavioral datasets
• Practical understanding of latent variable and dimensionality reduction models for multi-population data
• Experience with reproducible, collaborative scientific computing and open science practices
• Direct mentorship from staff scientists, exposure to systems neuroscience as a research area, and development of scientific writing and presentation skills

Required Education And Experience

• Currently enrolled in a master’s or PhD program in electrical and computer engineering, computer science, or a related quantitative field
• Completed coursework, prior to the start of the position, in programming, linear algebra, probability and statistics, and at least one of machine learning or signal processing
• Demonstrated experience with scientific computing in Python (numpy, scipy, pandas)
• Available to start in late October / early November 2026 and able to commit to the position for one year
• Able to work onsite at least one day per week

Preferred Education And Experience

• Coursework or project experience with linear regression and discrete classifiers (e.g., support vector machines)
• Coursework or project experience with dimensionality reduction and latent variable models (e.g., PCA, factor analysis, canonical correlation analysis)
• Experience with time series analysis and signal processing (filtering, spectral methods)
• Experience with scikit-learn and with deep learning frameworks such as PyTorch
• Experience with video‑based pose estimation tools (e.g., DeepLabCut, SLEAP, Lightning Pose) or with biosignal analysis such as EMG
• Experience with software best practices (version control, code review, testing)
• Interest in computational or systems neuroscience; prior neuroscience coursework or research is welcome but not required
• Strong written and verbal communication skills, and the ability to work both independently and in a collaborative, multi‑disciplinary environment

Physical Demands

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

Position Type/Expected Hours of Work

• During academic school year: part‑time, up to 19 hours per week
• During academic summer break: full‑time, approximately 40 hours per week
• Weekly hours are flexible and can be scheduled around academic coursework
• One-year fixed‑duration term position
• 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
• Able to work onsite at least one day per week

Salary

• $38.00 per hour (non‑negotiable)

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.