CPT Machine Learning Manager Jobs
Machine Learning Manager roles qualify for CPT when the work integrates directly into your degree program, your DSO must authorize each position before you start. These roles sit at the intersection of applied research and team leadership, so your CPT paperwork should reflect both the technical and managerial components of the job.
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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 Manager CPT: Frequently Asked Questions
Does a Machine Learning Manager role qualify for CPT?
Yes, if the position integrates directly into your enrolled degree program and your DSO authorizes it before your start date. Machine Learning Manager roles in graduate programs covering AI systems, model lifecycle management, or data engineering typically satisfy the curricular integration requirement. Your DSO will review the job offer and confirm how the responsibilities map to your coursework.
Can I do CPT as a Machine Learning Manager while still taking classes?
Yes. CPT can be part-time (20 hours or fewer per week) or full-time (more than 20 hours) depending on your program structure. Part-time CPT is common during active semesters for roles like Machine Learning Manager, since the job demands can be heavy. Confirm with your DSO whether your program permits full-time CPT while enrolled and whether your academic credits require concurrent enrollment.
How do I find employers offering CPT-compatible Machine Learning Manager positions?
Search Migrate Mate for Machine Learning Manager roles posted by employers with F-1 work authorization experience. Because CPT authorization happens at the school level rather than requiring employer sponsorship, your focus should be on employers familiar with hiring F-1 students and willing to provide the offer letter documentation your DSO needs to process your CPT.
Does CPT as a Machine Learning Manager count toward OPT eligibility?
Part-time CPT doesn't affect your OPT eligibility regardless of duration. Full-time CPT used for 12 or more cumulative months removes your OPT eligibility entirely under USCIS regulations. Machine Learning Manager roles often involve full-time commitments, so if you're planning to use OPT after graduation, track your full-time CPT months carefully and discuss the tradeoff with your DSO before accepting any full-time offer.
What documentation does my employer need to provide for my CPT authorization?
Your DSO typically requires a formal offer letter that specifies your job title, start and end dates, work location, hours per week, and a description of your responsibilities. For a Machine Learning Manager role, the description should reference tasks that align with your degree program, such as overseeing model development workflows, leading applied research teams, or managing ML infrastructure projects. Generic offer letters without role-specific detail can delay or block DSO approval.