H-1B1 Chile Visa Machine Learning Jobs
Machine Learning jobs in the U.S. are open to Chilean professionals through the H-1B1 Chile visa, a no-lottery work visa under the U.S.-Chile Free Trade Agreement. With an annual cap of 1,400 visas that rarely fills and consulate-direct processing, securing sponsorship for ML roles is faster and more predictable than the H-1B visa path.
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Get Access To All JobsINTRODUCTION
Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you’ll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One.
ROLE AND RESPONSIBILITIES
- The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following:
- Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams
- Build and scale massive multi-tenant platforms that enable running large footprint ML model training and/or serving at scale
- Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation
- Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
- Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications
- Retrain, maintain, and monitor models in production
- Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale
- Construct optimized data pipelines to feed ML models
- Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code
- Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI
- Use programming languages like Python, Scala, or Java
BASIC QUALIFICATIONS
- Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
- At least 6 years of experience programming with Python, Java, Golang, or C++
- At least 6 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn)
- At least 6 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data
- At least 4 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems
PREFERRED QUALIFICATIONS
- Master's or doctoral degree in computer science, electrical engineering, mathematics, or related field
- 5+ years of experience optimizing ML algorithms, configurations, and infrastructure
- 5+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc.
- 5+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans
- 5+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting)
- 5+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models
- ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents
- Ability to communicate complex technical and machine learning concepts clearly to a variety of audiences
Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
COMPENSATION
The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.
- McLean, VA: $229,900 - $262,400 for Machine Learning Engineer 5
- New York, NY: $250,800 - $286,200 for Machine Learning Engineer 5
- Plano, TX: $209,000 - $238,500 for Machine Learning Engineer 5
- Richmond, VA: $209,000 - $238,500 for Machine Learning Engineer 5
Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate’s offer letter.
This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.
Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.
This role is expected to accept applications for a minimum of 5 business days.
No agencies please.
Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City’s Fair Chance Act; Philadelphia’s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.
If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.
For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com.
Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site.
Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
INTRODUCTION
Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you’ll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One.
ROLE AND RESPONSIBILITIES
- The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following:
- Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams
- Build and scale massive multi-tenant platforms that enable running large footprint ML model training and/or serving at scale
- Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation
- Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
- Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications
- Retrain, maintain, and monitor models in production
- Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale
- Construct optimized data pipelines to feed ML models
- Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code
- Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI
- Use programming languages like Python, Scala, or Java
BASIC QUALIFICATIONS
- Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
- At least 6 years of experience programming with Python, Java, Golang, or C++
- At least 6 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn)
- At least 6 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data
- At least 4 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems
PREFERRED QUALIFICATIONS
- Master's or doctoral degree in computer science, electrical engineering, mathematics, or related field
- 5+ years of experience optimizing ML algorithms, configurations, and infrastructure
- 5+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc.
- 5+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans
- 5+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting)
- 5+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models
- ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents
- Ability to communicate complex technical and machine learning concepts clearly to a variety of audiences
Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
COMPENSATION
The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.
- McLean, VA: $229,900 - $262,400 for Machine Learning Engineer 5
- New York, NY: $250,800 - $286,200 for Machine Learning Engineer 5
- Plano, TX: $209,000 - $238,500 for Machine Learning Engineer 5
- Richmond, VA: $209,000 - $238,500 for Machine Learning Engineer 5
Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate’s offer letter.
This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.
Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.
This role is expected to accept applications for a minimum of 5 business days.
No agencies please.
Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City’s Fair Chance Act; Philadelphia’s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.
If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.
For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com.
Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site.
Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
See all 315+ H-1B1 Chile Visa Machine Learning Jobs
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Get Access To All JobsTips for Finding Machine Learning Jobs
Verify your ML degree matches the role
H-1B1 Chile requires a specialty occupation directly tied to your degree field. A computer science or statistics degree supports most ML roles cleanly, but an unrelated degree paired with ML experience alone won't satisfy the requirement without documented equivalency.
Translate your Chilean credentials into U.S. terms
Get your Chilean university transcripts evaluated by a NACES-accredited credential evaluator before applying. U.S. employers and consular officers need a formal equivalency statement confirming your degree meets the four-year U.S. bachelor's standard for ML specialty occupation roles.
Target employers with active H-1B1 Chile LCA history
Search the OFLC Wage Search to find companies that have filed Labor Condition Applications for ML job titles. Prior LCA filings signal that the employer's legal team already understands H-1B1 Chile sponsorship and won't stall at the offer stage.
Use Migrate Mate to find H-1B1 Chile sponsoring employers
Filter your ML job search by H-1B1 Chile sponsorship history on Migrate Mate. The platform surfaces employers with verified DOL filing records for Chilean nationals, so you're not cold-applying to companies that have never run this process before.
Negotiate LCA filing into your offer timeline
Your employer must file and receive a certified LCA from DOL before your consulate appointment. Build at least 10 business days for LCA certification into your start-date negotiation so the paperwork doesn't push your appointment past your agreed first day.
Prepare your prevailing wage documentation before the interview
Consular officers can ask how your offered salary compares to the DOL prevailing wage for your ML role and work location. Check the O*NET occupation code for your position and cross-reference the wage level in the OFLC Wage Search before your appointment.
Frequently Asked Questions
Does a Machine Learning role qualify as a specialty occupation for the H-1B1 Chile visa?
Yes. Machine Learning engineer, ML researcher, and applied scientist roles qualify because they require at minimum a bachelor's degree in computer science, mathematics, statistics, or a directly related field. The degree-to-job-duty connection must be explicit in the employer's job description and in the LCA filing to satisfy the specialty occupation standard at the consulate.
How does H-1B1 Chile compare to H-1B for Machine Learning professionals?
H-1B1 Chile has no lottery, an annual cap of 1,400 visas that has never been exhausted, and is processed directly at the U.S. consulate in Chile rather than through USCIS. The tradeoff is that H-1B1 Chile does not permit dual intent, so it's suited for professionals planning to work in the U.S. while maintaining Chilean ties, unlike the H-1B which allows immigrant intent.
How do I find U.S. employers willing to sponsor H-1B1 Chile visas for ML roles?
Migrate Mate lets you search for ML jobs filtered specifically by H-1B1 Chile sponsorship history, surfacing employers with verified DOL Labor Condition Application filings for Chilean nationals. This removes the guesswork of identifying companies already familiar with the H-1B1 Chile process versus those that would be running it for the first time.
Can I work for a U.S. employer on H-1B1 Chile while living in Chile?
No. The H-1B1 Chile visa authorizes in-person employment at a qualifying U.S. worksite. Remote-only arrangements where you remain in Chile don't satisfy the visa's admission and employment requirements. You must be physically present in the U.S. and working at the petitioned location to maintain valid H-1B1 Chile status.
What happens to my H-1B1 Chile status if my Machine Learning role is reclassified or my employer changes my job duties significantly?
A material change in job duties, title, or work location typically requires your employer to file an amended LCA with DOL. If the amended role no longer meets the specialty occupation definition tied to your original filing, your H-1B1 Chile status may be invalid until the new LCA is certified. Confirm any role changes with your employer's immigration counsel before they take effect.