Green Card Machine Learning Research Jobs
Machine Learning Research roles qualify for EB-2 sponsorship when they require an advanced degree in computer science, statistics, or a related field, and many employers file PERM labor certifications for these positions. Demand for ML researchers with specialized credentials makes green card sponsorship common at research-focused technology and enterprise organizations.
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Get Access To All JobsMachine Learning Research Engineer (ScientificEngineering AI)
Urgent Hiring Requirement
Minimum Qualification: PhD in a relevant technical field.
This is an urgent requirement with an anticipated start date within 2 weeks. Priority will be given to candidates who can interview promptly and begin within two weeks of selection.
Job Summary
We are seeking a highly motivated Machine Learning Research Engineer (ScientificEngineering AI) with strong expertise in Machine Learning, Deep Learning, Computer Vision, and AI research. This role is intended exclusively for PhD graduates or candidates near completion from reputable universities.
Candidates with a strong academic research background in Machine Learning, Artificial Intelligence, Computer Vision, Data Science, Scientific Computing, Mechanical Engineering, Materials Science, Manufacturing Engineering, Applied Physics, Computational Engineering, or related fields are encouraged to apply.
Ideal candidates will combine strong ML/DL expertise with domain knowledge in mechanical engineering, materials science, manufacturing systems, physical systems, scientific computing, or simulation-driven engineering applications.
Research experience gained during a PhD program will be considered equivalent to professional industry experience.
This is an urgent hiring requirement, and we are actively seeking candidates who can start within the next 2 weeks.
Education Requirement
PhD in Computer Science, Computer Engineering, Electrical Engineering, Artificial Intelligence, Machine Learning, Data Science, Mechanical Engineering, Materials Science, Manufacturing Engineering, Applied Physics, Computational Engineering, or a related technical field.
Candidates currently pursuing a PhD with anticipated graduation within the next 3-6 months are also encouraged to apply.
Only PhD candidates will be considered for this role.
Candidates with only a Master's degree will not be considered.
Key Responsibilities
- Design, develop, train, and optimize Machine Learning and Deep Learning models for real-world applications.
- Own the complete ML lifecycle including data collection, annotation, preprocessing, model training, fine-tuning, evaluation, optimization, and deployment.
- Develop and deploy advanced deep learning architectures including CNNs, LSTMs, ConvLSTMs, Graph Neural Networks (GNNs), Reinforcement Learning, and Transformer-based models.
- Conduct experiments, evaluate model performance, and drive continuous algorithmic improvements.
- Work with large-scale datasets for model training, validation, and testing.
- Optimize and deploy AI models for scalable and efficient real-world applications.
- Translate research concepts into scalable, production-ready AI systems.
- Collaborate with cross-functional engineering and research teams to integrate ML models into real-world applications.
- Document methodologies, experimental findings, and technical solutions.
- Contribute to technical innovation initiatives and advanced AI research activities.
Required Qualifications
- Strong PhD research background in Machine Learning, Deep Learning, Artificial Intelligence, Computer Vision, Data Science, Scientific Machine Learning, Computational Engineering, Applied Physics, Materials Informatics, or related areas.
- Strong programming experience with Python and C++.
- Hands-on experience with PyTorch, TensorFlow, Keras, Scikit-learn, or similar ML frameworks.
- Strong understanding of Machine Learning, Deep Learning, Neural Networks, Computer Vision, and AI algorithms.
- Experience developing and training advanced deep learning models and architectures.
- Solid mathematical foundation in linear algebra, probability, statistics, optimization, and applied machine learning.
- Experience working with Linux environments, Git, Docker, and modern development workflows.
- Demonstrated research experience through publications, thesis work, academic research projects, or equivalent research contributions.
- Strong ability to independently research, prototype, and deploy AI solutions.
- Experience applying machine learning or deep learning techniques to engineering, manufacturing, materials science, physical systems, scientific computing, simulation, or industrial applications is highly desirable.
Preferred Qualifications
- Publications in leading AI, Machine Learning, Computer Science, Scientific Computing, Computational Engineering, Materials Science, or Applied Physics conferences and journals.
- Experience transitioning AI/ML models from research environments into production systems.
- Experience with CUDA, GPU acceleration, distributed computing, high-performance computing (HPC), or parallel computing environments.
- Experience handling large-scale, real-world datasets.
- Familiarity with Physics-Informed Machine Learning (PIML), Physics-Informed Neural Networks (PINNs), scientific foundation models, digital twins, simulation-driven AI, or engineering optimization techniques.
- Experience working with data generated from CAD, CAE, CFD, FEA, multiphysics simulations, manufacturing processes, materials characterization, laboratory testing, or other engineering and scientific workflows.
Technical Skills
- Python, C++
- PyTorch, TensorFlow, Keras, Scikit-learn
- Machine Learning and Deep Learning
- Computer Vision
- Reinforcement Learning
- Graph Neural Networks (GNNs)
- Transformer Architectures
- Linux, Git, Docker
- CUDA and GPU Computing
- Scientific Computing and Optimization
- Physics-Informed Machine Learning (Preferred)
- Engineering and Scientific Data Analysis (Preferred)
Machine Learning Research Engineer (ScientificEngineering AI)
Urgent Hiring Requirement
Minimum Qualification: PhD in a relevant technical field.
This is an urgent requirement with an anticipated start date within 2 weeks. Priority will be given to candidates who can interview promptly and begin within two weeks of selection.
Job Summary
We are seeking a highly motivated Machine Learning Research Engineer (ScientificEngineering AI) with strong expertise in Machine Learning, Deep Learning, Computer Vision, and AI research. This role is intended exclusively for PhD graduates or candidates near completion from reputable universities.
Candidates with a strong academic research background in Machine Learning, Artificial Intelligence, Computer Vision, Data Science, Scientific Computing, Mechanical Engineering, Materials Science, Manufacturing Engineering, Applied Physics, Computational Engineering, or related fields are encouraged to apply.
Ideal candidates will combine strong ML/DL expertise with domain knowledge in mechanical engineering, materials science, manufacturing systems, physical systems, scientific computing, or simulation-driven engineering applications.
Research experience gained during a PhD program will be considered equivalent to professional industry experience.
This is an urgent hiring requirement, and we are actively seeking candidates who can start within the next 2 weeks.
Education Requirement
PhD in Computer Science, Computer Engineering, Electrical Engineering, Artificial Intelligence, Machine Learning, Data Science, Mechanical Engineering, Materials Science, Manufacturing Engineering, Applied Physics, Computational Engineering, or a related technical field.
Candidates currently pursuing a PhD with anticipated graduation within the next 3-6 months are also encouraged to apply.
Only PhD candidates will be considered for this role.
Candidates with only a Master's degree will not be considered.
Key Responsibilities
- Design, develop, train, and optimize Machine Learning and Deep Learning models for real-world applications.
- Own the complete ML lifecycle including data collection, annotation, preprocessing, model training, fine-tuning, evaluation, optimization, and deployment.
- Develop and deploy advanced deep learning architectures including CNNs, LSTMs, ConvLSTMs, Graph Neural Networks (GNNs), Reinforcement Learning, and Transformer-based models.
- Conduct experiments, evaluate model performance, and drive continuous algorithmic improvements.
- Work with large-scale datasets for model training, validation, and testing.
- Optimize and deploy AI models for scalable and efficient real-world applications.
- Translate research concepts into scalable, production-ready AI systems.
- Collaborate with cross-functional engineering and research teams to integrate ML models into real-world applications.
- Document methodologies, experimental findings, and technical solutions.
- Contribute to technical innovation initiatives and advanced AI research activities.
Required Qualifications
- Strong PhD research background in Machine Learning, Deep Learning, Artificial Intelligence, Computer Vision, Data Science, Scientific Machine Learning, Computational Engineering, Applied Physics, Materials Informatics, or related areas.
- Strong programming experience with Python and C++.
- Hands-on experience with PyTorch, TensorFlow, Keras, Scikit-learn, or similar ML frameworks.
- Strong understanding of Machine Learning, Deep Learning, Neural Networks, Computer Vision, and AI algorithms.
- Experience developing and training advanced deep learning models and architectures.
- Solid mathematical foundation in linear algebra, probability, statistics, optimization, and applied machine learning.
- Experience working with Linux environments, Git, Docker, and modern development workflows.
- Demonstrated research experience through publications, thesis work, academic research projects, or equivalent research contributions.
- Strong ability to independently research, prototype, and deploy AI solutions.
- Experience applying machine learning or deep learning techniques to engineering, manufacturing, materials science, physical systems, scientific computing, simulation, or industrial applications is highly desirable.
Preferred Qualifications
- Publications in leading AI, Machine Learning, Computer Science, Scientific Computing, Computational Engineering, Materials Science, or Applied Physics conferences and journals.
- Experience transitioning AI/ML models from research environments into production systems.
- Experience with CUDA, GPU acceleration, distributed computing, high-performance computing (HPC), or parallel computing environments.
- Experience handling large-scale, real-world datasets.
- Familiarity with Physics-Informed Machine Learning (PIML), Physics-Informed Neural Networks (PINNs), scientific foundation models, digital twins, simulation-driven AI, or engineering optimization techniques.
- Experience working with data generated from CAD, CAE, CFD, FEA, multiphysics simulations, manufacturing processes, materials characterization, laboratory testing, or other engineering and scientific workflows.
Technical Skills
- Python, C++
- PyTorch, TensorFlow, Keras, Scikit-learn
- Machine Learning and Deep Learning
- Computer Vision
- Reinforcement Learning
- Graph Neural Networks (GNNs)
- Transformer Architectures
- Linux, Git, Docker
- CUDA and GPU Computing
- Scientific Computing and Optimization
- Physics-Informed Machine Learning (Preferred)
- Engineering and Scientific Data Analysis (Preferred)
See all 359+ Green Card Machine Learning Research Jobs
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Get Access To All JobsTips for Finding Green Card Sponsorship in Machine Learning Research
Document your research contributions before applying
Compile published papers, patents, conference presentations, and citations into a single credentials file. PERM and I-140 adjudicators treat documented research output as evidence of specialized qualifications that justify a permanent hire over a U.S. worker.
Target employers with active PERM filing history
Search DOL OFLC disclosure data for employers who have filed PERM applications under ML and AI job titles. Repeated filings signal an established sponsorship process, which reduces the risk of an offer expiring before the I-140 clears.
Confirm the prevailing wage tier before negotiating
Look up the wage level for your specific O*NET occupation code using the OFLC Wage Search before accepting an offer. Your employer must pay at or above the certified wage throughout the green card process, so Level III or IV designations affect your long-term compensation floor.
Use Migrate Mate to filter for green card sponsoring roles
Search for Machine Learning Research positions by sponsorship type on Migrate Mate, which surfaces employers with documented EB-2 and EB-3 filing history. This narrows your list to companies that have already navigated the PERM process for similar roles.
Clarify whether EB-2 or EB-3 applies to your offer
If the job description requires only a bachelor's degree, your employer may file under EB-3 even for senior research roles. Ask the HR team or immigration counsel which category they intend to file under, since EB-2 priority dates for some countries move significantly faster.
Understand how PERM recruitment affects your start date
USCIS requires employers to complete a supervised DOL recruitment period before certifying the PERM application. For ML Research roles, that process typically adds several months before the I-140 can be filed, so factor this into your employment start timeline.
Green Card Machine Learning Research: Frequently Asked Questions
Do Machine Learning Research roles commonly qualify for EB-2 green card sponsorship?
Yes. Most ML Research positions require a master's or doctoral degree in computer science, statistics, or a related quantitative field, which satisfies the EB-2 advanced-degree requirement. Employers must still complete the PERM labor certification before filing the I-140 petition, confirming no qualified U.S. worker is available for the specific role.
How does green card sponsorship differ from H-1B for ML Research jobs?
The H-1B visa is a temporary nonimmigrant visa subject to an annual lottery cap, while EB-2 and EB-3 green card sponsorship leads to permanent residency with no lottery. The tradeoff is timeline: PERM labor certification and I-140 adjudication often take one to three years before priority date movement becomes relevant, compared to an H-1B that can authorize work within months.
Which employers typically sponsor green cards for Machine Learning Research positions?
Large technology companies, pharmaceutical and biotech firms, financial institutions with quantitative research divisions, and university-affiliated research labs regularly file PERM applications for ML researchers. DOL OFLC disclosure data is a reliable way to verify which organizations have filed for comparable job titles before you apply.
How can I find Machine Learning Research jobs that include green card sponsorship?
Migrate Mate lets you filter job listings by employment-based sponsorship category, surfacing EB-2 and EB-3 opportunities from employers with documented PERM filing history for ML and AI roles. This is more targeted than general job searches because it filters specifically for green card sponsorship rather than temporary work visa support.
Does my country of birth affect how long EB-2 or EB-3 sponsorship takes for ML Research roles?
Yes, significantly. Nationals of India and China face priority date backlogs measured in years or decades for EB-2 and EB-3 due to per-country caps on annual green card issuances. Nationals from most other countries fall under the Rest of World queue, where priority dates for ML Research roles typically move much faster after PERM and I-140 approval.