CPT Machine Learning Intern Jobs
Machine Learning Intern jobs on CPT let you apply neural networks, model training, and data pipeline work directly inside your degree program. Your DSO must authorize each CPT position before you start, and the role must connect to your enrolled coursework. Full-time CPT is available but counts against your OPT eligibility if used for 12 months or more.
Find CPT Machine Learning Intern JobsOverview
Showing 5 of 76+ Machine Learning Intern jobs
See all 76+ Machine Learning Intern Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Machine Learning Intern roles.
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.
See all 76+ CPT Machine Learning Intern Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new CPT Machine Learning Intern Jobs.
Get Access To All JobsMachine Learning Intern CPT: Frequently Asked Questions
Does a Machine Learning Intern role qualify for CPT?
Yes, if the work directly relates to your enrolled program. ML intern duties like model training, dataset curation, and experiment tracking map cleanly to graduate programs in computer science, data science, and statistics. Your DSO makes the final determination, so bring the full job description to your authorization meeting and highlight the technical overlap with your coursework.
Can I do full-time CPT as a Machine Learning Intern?
You can, but full-time CPT carries a significant trade-off: 12 or more months of full-time CPT eliminates your OPT eligibility entirely. Part-time CPT of 20 hours or fewer per week doesn't affect OPT. Most F-1 students in ML roles structure their first CPT as part-time to preserve post-graduation options, then decide on full-time only if a specific program or thesis requires it.
How do I find employers that hire CPT students for ML internships?
Migrate Mate lets you filter for employers with a history of hiring F-1 students in machine learning and data science roles. Research labs, AI-focused startups, and large tech companies are the most active CPT employers in this space. Confirm before applying that the employer understands CPT work authorization and doesn't require H-1B visa sponsorship as a precondition for intern hiring.
Does my employer need to file anything with USCIS for CPT?
No. CPT is authorized entirely through your school and DSO, not through USCIS. Your employer doesn't file a petition or pay a government fee. The only paperwork your employer handles is the standard I-9 employment eligibility verification, for which your CPT-endorsed I-20 and valid F-1 visa or other acceptable document serve as the authorization.
What happens if my ML internship extends beyond the semester listed on my I-20?
You must get a new CPT authorization from your DSO before the extension start date. Working past the end date on your current I-20 without an updated authorization is a status violation. Contact your DSO at least two weeks before the original end date, provide the amended offer letter or extension confirmation, and don't continue working until the updated I-20 is in hand.