CPT Machine Learning Jobs
Machine Learning CPT jobs let F-1 students apply neural network design, model training, and data pipeline work directly to a degree program requirement. Your DSO must authorize each position before you start, and the role must connect to your coursework. Part-time CPT runs up to 20 hours per week during the semester; full-time is available during breaks.
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Get Access To All JobsResearch 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.
A cover letter is required to be considered for this position.
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.
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.
A cover letter is required to be considered for this position.
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.
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Get Access To All JobsMachine Learning CPT: Frequently Asked Questions
Does a Machine Learning CPT job have to involve coding or model building specifically?
Not exclusively, but the work must tie directly to your degree program's learning objectives. A role focused on ML research, data labeling pipelines, or model evaluation qualifies as long as your DSO can connect it to an enrolled course. Purely administrative or IT support roles at an ML company won't meet the integral-part-of-curriculum standard.
Can I do CPT at a startup that doesn't have an HR department?
Yes. CPT authorization comes from your school's DSO, not the employer. The employer doesn't file anything with USCIS. You do need the employer to sign an offer letter confirming the role, start date, hours, and supervisor. Startups can do this. Confirm before your DSO meeting that the letter is on company letterhead with a named supervisor.
How do I find Machine Learning CPT jobs with employers who understand F-1 authorization?
Migrate Mate lists Machine Learning roles with employers that have prior sponsorship filing history, which is a strong signal they've navigated F-1 work authorization before. Filter by role type and review employer filing data before applying to avoid companies that will stall your onboarding over authorization questions.
Does working a Machine Learning CPT role full-time over the summer count against my 12-month limit?
Yes. Full-time CPT during a summer session counts toward the 12-month ceiling. If you accumulate 12 or more months of full-time CPT across your entire F-1 program, you lose OPT eligibility entirely. Part-time CPT, including summer part-time, doesn't count toward that limit regardless of how long you work.
What O*NET occupation code covers most Machine Learning CPT roles?
Most ML roles fall under O*NET code 15-2051.00, Data Scientists, or 15-1221.00, Computer and Information Research Scientists, depending on the work's emphasis. Your DSO may reference these codes when evaluating whether the role qualifies. Checking the O*NET profile for your specific job title confirms the typical education requirement, which strengthens your CPT authorization request.