CPT Machine Learning Engineer Jobs
Machine Learning Engineer CPT jobs let F-1 students apply neural network design, model training, and MLOps skills in a role your DSO can authorize as an integral part of your curriculum. Your I-20 must reflect an ML-related program, and your school typically requires a training plan before authorizing CPT.
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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 Engineer CPT: Frequently Asked Questions
Does a Machine Learning Engineer role qualify for CPT?
Yes, if your academic program covers machine learning, AI, or data science and the role directly applies those skills. Your DSO evaluates whether the specific job duties, not just the title, connect to your curriculum. Roles focused on model training, feature engineering, or ML infrastructure typically qualify; general software engineering roles with minimal ML work may not.
Can I do full-time CPT as an MLE without affecting OPT eligibility?
You can do full-time CPT, but 12 or more months of full-time CPT eliminates your OPT eligibility entirely. Part-time CPT, defined as 20 hours or fewer per week, doesn't count toward that limit regardless of how many semesters you complete it. Most F-1 students in MLE roles use part-time CPT during the semester and switch to full-time in summer to preserve OPT.
What documents do I need to start a CPT Machine Learning Engineer job?
You need an updated I-20 with the CPT authorization endorsed by your DSO, a signed offer letter listing your ML-specific job duties, a training plan approved by your academic advisor, and in most cases proof of enrollment for the term covering your CPT dates. Some schools also require a faculty sponsor or department approval for technical roles like MLE before the DSO will sign off.
How do I find Machine Learning Engineer employers who hire CPT students?
Use Migrate Mate to browse MLE roles posted by employers with a documented history of hiring F-1 students on work authorization. Many MLE postings on general job boards exclude work authorization candidates in the initial screening, so filtering for CPT-aware employers early saves significant time and avoids applications that stall after recruiter calls.
Does my MLE CPT job need to be related to my exact major?
Your CPT must be an integral part of an established curriculum, which means the role must relate to your field of study, not necessarily your exact major title. A computer science student can typically qualify for an MLE CPT role. A student in an unrelated program, even one with ML coursework as electives, may face DSO pushback. The O*NET occupation profile for Machine Learning Engineers can help you document the technical skill overlap during your DSO meeting.