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
Consumer & Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We're proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions – all while ranking first in customer satisfaction. In this role, you'll apply strong technical judgment to choose the right approaches (including modern LLM-based methods where appropriate), evaluate performance with rigorous metrics, and ensure solutions are reliable, secure, and scalable in real-world environments. You'll also contribute to improving data quality and feedback loops, monitoring models in production, and continuously iterating to reduce agent effort, shorten resolution times, and increase consistency and quality across operational workflows. As a Machine Learning Engineer-Digital intelligence in the Consumer & Community Banking division, you will be collaborating with a high-caliber team of software developers and deep learning experts, and you will specialize in large language modeling, optimization, interpretability, and related algorithms. The ideal candidate brings a strong software engineering foundation combined with hands-on, zero-to-one machine learning development experience. You will possess broad expertise in post-training machine learning models — including quality and performance optimization — alongside deep knowledge of large language models and modern deep learning techniques. Above all, you will have a demonstrated ability to operate at the intersection of research and engineering, turning promising ideas into scalable, real-world products within a fast-paced, collaborative environment.
JOB RESPONSIBILITIES
- Research and prototype next-generation architectures for structured and unstructured data
- Develop novel pre-training objectives tailored to financial event sequences and heterogeneous profile data
- Implement research ideas in production-quality code
- Mentor engineers on ML best practices; translate research advances into deployable systems
- Optimize training throughput for large data sources
- Collaborate with other teams to design solutions for product use cases.
BASIC QUALIFICATIONS
- Master's degree with 2+ years Or Bachelor's with 4+ years in Computer Science, with training and work experience in Machine Learning, LLM/NLP or similar fields.
- Deep LLM and Transformer expertise — strong command of attention mechanisms, positional encodings such as RoPE, and the ability to handle multi-modal data inputs effectively.
- PyTorch proficiency at scale — hands-on experience with distributed training frameworks including FSDP and DeepSpeed, alongside practical memory optimization techniques.
- Foundation model training — proven experience in pre-training from scratch and designing tokens and vocabularies for complex, heterogeneous data sources including tabular, temporal, and graphical formats.
- Strong software engineering skills — ability to build robust, production-quality systems that perform reliably at scale.
- Prior experience with financial data and recommendation systems.
PREFERRED QUALIFICATIONS
- Publication record at top AI/ML venues.
- Experience optimizing serving infrastructure is a plus.
- Experience with post-training LLMs and network optimization algorithms, as well as interpretability or steering techniques for LLMs.
- Experience working with large-scale compute infrastructure.
- Experience shipping a real-world product, project, or feature.
INTRODUCTION
Consumer & Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We're proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions – all while ranking first in customer satisfaction. In this role, you'll apply strong technical judgment to choose the right approaches (including modern LLM-based methods where appropriate), evaluate performance with rigorous metrics, and ensure solutions are reliable, secure, and scalable in real-world environments. You'll also contribute to improving data quality and feedback loops, monitoring models in production, and continuously iterating to reduce agent effort, shorten resolution times, and increase consistency and quality across operational workflows. As a Machine Learning Engineer-Digital intelligence in the Consumer & Community Banking division, you will be collaborating with a high-caliber team of software developers and deep learning experts, and you will specialize in large language modeling, optimization, interpretability, and related algorithms. The ideal candidate brings a strong software engineering foundation combined with hands-on, zero-to-one machine learning development experience. You will possess broad expertise in post-training machine learning models — including quality and performance optimization — alongside deep knowledge of large language models and modern deep learning techniques. Above all, you will have a demonstrated ability to operate at the intersection of research and engineering, turning promising ideas into scalable, real-world products within a fast-paced, collaborative environment.
JOB RESPONSIBILITIES
- Research and prototype next-generation architectures for structured and unstructured data
- Develop novel pre-training objectives tailored to financial event sequences and heterogeneous profile data
- Implement research ideas in production-quality code
- Mentor engineers on ML best practices; translate research advances into deployable systems
- Optimize training throughput for large data sources
- Collaborate with other teams to design solutions for product use cases.
BASIC QUALIFICATIONS
- Master's degree with 2+ years Or Bachelor's with 4+ years in Computer Science, with training and work experience in Machine Learning, LLM/NLP or similar fields.
- Deep LLM and Transformer expertise — strong command of attention mechanisms, positional encodings such as RoPE, and the ability to handle multi-modal data inputs effectively.
- PyTorch proficiency at scale — hands-on experience with distributed training frameworks including FSDP and DeepSpeed, alongside practical memory optimization techniques.
- Foundation model training — proven experience in pre-training from scratch and designing tokens and vocabularies for complex, heterogeneous data sources including tabular, temporal, and graphical formats.
- Strong software engineering skills — ability to build robust, production-quality systems that perform reliably at scale.
- Prior experience with financial data and recommendation systems.
PREFERRED QUALIFICATIONS
- Publication record at top AI/ML venues.
- Experience optimizing serving infrastructure is a plus.
- Experience with post-training LLMs and network optimization algorithms, as well as interpretability or steering techniques for LLMs.
- Experience working with large-scale compute infrastructure.
- Experience shipping a real-world product, project, or feature.
See all 381+ 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.