H-1B1 Chile Visa ML Engineer Jobs
ML Engineer jobs with H-1B1 Chile visa sponsorship are open to Chilean nationals under the U.S.-Chile Free Trade Agreement. No lottery, no USCIS filing, and the 1,400-visa annual cap rarely fills. Employers file a Labor Condition Application with DOL, and you apply directly at the consulate with your job offer in hand.
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Re-architecting Engineering for the Age of Intelligence
PhysicsX is the physics AI company for industrials. The company’s mission is to accelerate hardware innovation by overhauling what industrial engineering and manufacturing look like today. PhysicsX is building a new simulation software stack to deliver deep physics AI enablement across the entire engineering lifecycle. The company partners with leading organisations in aerospace & defence, automotive, semiconductors, materials, and energy & renewables, supporting them on some of their most critical and complex challenges. PhysicsX is headquartered in the United Kingdom, with offices in London, New York, and Singapore and an expanding presence in the Bay Area.
Who We're Looking For
As a Principal Machine Learning Engineer in Delivery, you are an experienced problem solver and technical leader who stays anchored to impact. You can grasp advanced engineering concepts across multiple industries, lead technical initiatives, and work directly with customers (often side by side on-site) to embed cutting-edge AI models into tools that are useful and used. For this role, that means manufacturing and maintenance, repair and overhaul (MRO). You have put ML into production on industrial process or sensor data, not benchmarks. You know the difference between a model that scores well offline and one a process owner will accept on a qualified production line. Your data comes off equipment rather than out of a solver: it is sparse, drifting, miscalibrated and partly unrecorded, and you know how to work with it. You've shipped ML systems end to end and at scale: you design, build and test reliable, scalable ML data pipelines, select the right libraries, frameworks and tools, and make pragmatic product decisions that set Delivery up for success. Working at the intersection of data science and software engineering, you translate R&D and project outputs into reusable libraries, tooling and products. With at least 5 years of industry experience (post Masters or PhD) in a commercial, non-research environment, you're ready not only to execute but to lead and mentor others.
This Role
You'll work closely with Data Scientists, Simulation Engineers, Manufacturing Process Experts and customers to define the process and physics problems we are solving, and you'll drive decisions on reliable deployment with measurable outcomes on the shop floor.
You'll
- Own the ML approach to manufacturing and MRO data, from modelling through deployment in customer production environments (cloud, on-prem and edge).
- Build production ML on sparse, drifting, miscalibrated process and sensor data (process logs, sensor traces, inspection records, small DoE sets), and judge whether the data is enough to train on.
- Choose between physics simulation, reduced-order and learned models, and fuse simulation with shop-floor measurement where the two disagree.
- Set the UQ and validation standard for what a process recommendation may claim against a qualified process window, and design experiments where machine time is the cost.
- Make models transfer across machines, material lots and consumable batches.
- Define the product requirements that manufacturing and MRO place on the PhysicsX platform.
- Lead scoping and architecture for data and ML systems, and communicate results and trade-offs to senior stakeholders and process owners.
- Build end-to-end training and inference pipelines and APIs (Python, PyTorch, Pandas, FastAPI, SciPy, Kubeflow, among others), including for 3D point-cloud and mesh data, and mentor engineers and data scientists.
- Travel to customer sites worldwide, on average 3-4 weeks per quarter, and take part in scoping new projects and bringing new customers to PhysicsX.
What you've done
We hire on what you've done, so strong candidates can point to things like:
- Built and deployed ML on manufacturing process data (parameter logs, sensor traces, inspection records or small DoE sets) and can say what changed in production as a result.
- Fused simulation output with shop-floor measurement where the two disagreed.
- Set UQ and validation standards that other teams followed.
- Shaped a product or tooling roadmap based on what a site or company actually needed.
- Had a recommendation rejected by a process owner, and understood why.
Experience in aerospace or another regulated manufacturing environment is a plus. It is not required, because the must-have bar can be met in any sector where data comes off equipment.
Our delivery teams drive innovation to turn AI models into practical solutions - read our blog to learn more about how you’ll contribute to this exciting journey!
What We Offer
Build what actually matters
Help shape an AI-native engineering company at a formative stage, tackling problems that genuinely matter for industry and society. This is work with real-world impact - and something you can be proud to stand behind.
Learn alongside exceptional people
Work with a high-caliber, collaborative team of engineers, scientists, and operators who care deeply about doing great work, and about helping each other get better. We come from diverse backgrounds, but we share a commitment to operating at the highest level and addressing some of the most complex challenges out there. If you’re ambitious, thoughtful, and driven by impact, you’ll feel at home.
Influence over hierarchy
We operate with a flat structure: good ideas win - wherever they come from. Questioning assumptions and challenging the status quo isn’t just welcomed, it’s expected.
Sustainable pace, long-term ambition
Building meaningful technology is a marathon, not a sprint. We believe in balancing focused, ambitious work with a life beyond it. Our hybrid model blends time together in our New York office with work-from-home days, giving you the flexibility to work sustainably while staying connected in person.
And it doesn’t stop there …
🚀 Equity options - share meaningfully in the company you’re helping to build.
💰 5% contribution to 401(k) - build long-term security with a strong retirement plan.
🍽️ Free team lunch 1x/week - good food, great company, and space to connect.
🏥 Private health insurance – comprehensive cover for you, offering total peace of mind.
👶 Enhanced parental leave – 3 months full pay paternity and 6 months full pay maternity leave, to provide extra flexibility during the moments that matter most.
☀️ 20 days of Annual Leave (+ Public Holidays) - because taking time to rest matters.
📈 Personal development – dedicated support for learning, development, and leveling up over time.
💪 Gympass / Wellhub (subsidized) – for you and up to 3 family members, supporting both physical and mental wellbeing.
💳 Flexible Spending Account (FSA) – set aside pre-tax dollars for eligible healthcare expenses.
🔎 Watch this space, we’re continuing to build this as we grow…
Salary Range
$140,000 - $190,000 depending on experience. Seniority will be assessed throughout our interview process.
We value diversity and are committed to equal employment opportunity regardless of sex, race, religion, ethnicity, nationality, disability, age, sexual orientation or gender identity. We strongly encourage individuals from groups traditionally underrepresented in tech to apply. To help make a change, we sponsor bright women from disadvantaged backgrounds through their university degrees in science and mathematics. We collect diversity and inclusion data solely for the purpose of monitoring the effectiveness of our equal opportunities policies and ensuring compliance with employment and equality legislation. This information is confidential, used only in aggregate form, and will not influence the outcome of your application.
About us
Re-architecting Engineering for the Age of Intelligence
PhysicsX is the physics AI company for industrials. The company’s mission is to accelerate hardware innovation by overhauling what industrial engineering and manufacturing look like today. PhysicsX is building a new simulation software stack to deliver deep physics AI enablement across the entire engineering lifecycle. The company partners with leading organisations in aerospace & defence, automotive, semiconductors, materials, and energy & renewables, supporting them on some of their most critical and complex challenges. PhysicsX is headquartered in the United Kingdom, with offices in London, New York, and Singapore and an expanding presence in the Bay Area.
Who We're Looking For
As a Principal Machine Learning Engineer in Delivery, you are an experienced problem solver and technical leader who stays anchored to impact. You can grasp advanced engineering concepts across multiple industries, lead technical initiatives, and work directly with customers (often side by side on-site) to embed cutting-edge AI models into tools that are useful and used. For this role, that means manufacturing and maintenance, repair and overhaul (MRO). You have put ML into production on industrial process or sensor data, not benchmarks. You know the difference between a model that scores well offline and one a process owner will accept on a qualified production line. Your data comes off equipment rather than out of a solver: it is sparse, drifting, miscalibrated and partly unrecorded, and you know how to work with it. You've shipped ML systems end to end and at scale: you design, build and test reliable, scalable ML data pipelines, select the right libraries, frameworks and tools, and make pragmatic product decisions that set Delivery up for success. Working at the intersection of data science and software engineering, you translate R&D and project outputs into reusable libraries, tooling and products. With at least 5 years of industry experience (post Masters or PhD) in a commercial, non-research environment, you're ready not only to execute but to lead and mentor others.
This Role
You'll work closely with Data Scientists, Simulation Engineers, Manufacturing Process Experts and customers to define the process and physics problems we are solving, and you'll drive decisions on reliable deployment with measurable outcomes on the shop floor.
You'll
- Own the ML approach to manufacturing and MRO data, from modelling through deployment in customer production environments (cloud, on-prem and edge).
- Build production ML on sparse, drifting, miscalibrated process and sensor data (process logs, sensor traces, inspection records, small DoE sets), and judge whether the data is enough to train on.
- Choose between physics simulation, reduced-order and learned models, and fuse simulation with shop-floor measurement where the two disagree.
- Set the UQ and validation standard for what a process recommendation may claim against a qualified process window, and design experiments where machine time is the cost.
- Make models transfer across machines, material lots and consumable batches.
- Define the product requirements that manufacturing and MRO place on the PhysicsX platform.
- Lead scoping and architecture for data and ML systems, and communicate results and trade-offs to senior stakeholders and process owners.
- Build end-to-end training and inference pipelines and APIs (Python, PyTorch, Pandas, FastAPI, SciPy, Kubeflow, among others), including for 3D point-cloud and mesh data, and mentor engineers and data scientists.
- Travel to customer sites worldwide, on average 3-4 weeks per quarter, and take part in scoping new projects and bringing new customers to PhysicsX.
What you've done
We hire on what you've done, so strong candidates can point to things like:
- Built and deployed ML on manufacturing process data (parameter logs, sensor traces, inspection records or small DoE sets) and can say what changed in production as a result.
- Fused simulation output with shop-floor measurement where the two disagreed.
- Set UQ and validation standards that other teams followed.
- Shaped a product or tooling roadmap based on what a site or company actually needed.
- Had a recommendation rejected by a process owner, and understood why.
Experience in aerospace or another regulated manufacturing environment is a plus. It is not required, because the must-have bar can be met in any sector where data comes off equipment.
Our delivery teams drive innovation to turn AI models into practical solutions - read our blog to learn more about how you’ll contribute to this exciting journey!
What We Offer
Build what actually matters
Help shape an AI-native engineering company at a formative stage, tackling problems that genuinely matter for industry and society. This is work with real-world impact - and something you can be proud to stand behind.
Learn alongside exceptional people
Work with a high-caliber, collaborative team of engineers, scientists, and operators who care deeply about doing great work, and about helping each other get better. We come from diverse backgrounds, but we share a commitment to operating at the highest level and addressing some of the most complex challenges out there. If you’re ambitious, thoughtful, and driven by impact, you’ll feel at home.
Influence over hierarchy
We operate with a flat structure: good ideas win - wherever they come from. Questioning assumptions and challenging the status quo isn’t just welcomed, it’s expected.
Sustainable pace, long-term ambition
Building meaningful technology is a marathon, not a sprint. We believe in balancing focused, ambitious work with a life beyond it. Our hybrid model blends time together in our New York office with work-from-home days, giving you the flexibility to work sustainably while staying connected in person.
And it doesn’t stop there …
🚀 Equity options - share meaningfully in the company you’re helping to build.
💰 5% contribution to 401(k) - build long-term security with a strong retirement plan.
🍽️ Free team lunch 1x/week - good food, great company, and space to connect.
🏥 Private health insurance – comprehensive cover for you, offering total peace of mind.
👶 Enhanced parental leave – 3 months full pay paternity and 6 months full pay maternity leave, to provide extra flexibility during the moments that matter most.
☀️ 20 days of Annual Leave (+ Public Holidays) - because taking time to rest matters.
📈 Personal development – dedicated support for learning, development, and leveling up over time.
💪 Gympass / Wellhub (subsidized) – for you and up to 3 family members, supporting both physical and mental wellbeing.
💳 Flexible Spending Account (FSA) – set aside pre-tax dollars for eligible healthcare expenses.
🔎 Watch this space, we’re continuing to build this as we grow…
Salary Range
$140,000 - $190,000 depending on experience. Seniority will be assessed throughout our interview process.
We value diversity and are committed to equal employment opportunity regardless of sex, race, religion, ethnicity, nationality, disability, age, sexual orientation or gender identity. We strongly encourage individuals from groups traditionally underrepresented in tech to apply. To help make a change, we sponsor bright women from disadvantaged backgrounds through their university degrees in science and mathematics. We collect diversity and inclusion data solely for the purpose of monitoring the effectiveness of our equal opportunities policies and ensuring compliance with employment and equality legislation. This information is confidential, used only in aggregate form, and will not influence the outcome of your application.
See all 317+ H-1B1 Chile Visa ML Engineer Jobs
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Get Access To All JobsTips for Finding Visa Sponsorship as a ML Engineer
Verify your degree satisfies specialty occupation
The H-1B1 visa requires a bachelor's degree or higher in a field directly related to ML engineering. A degree in computer science, electrical engineering, or applied mathematics typically qualifies. A general management or unrelated STEM degree won't, even with years of ML experience.
Pull your O*NET occupation profile before applying
ML Engineer maps to O*NET occupation 15-2051.00 under Mathematical Science Occupations. Printing that profile and matching your responsibilities to the listed tasks gives your employer concrete language to justify specialty occupation status in the LCA.
Target employers already filing LCAs for technical roles
Search Migrate Mate to find companies with active Labor Condition Application history for software and ML roles. Employers who have filed before understand the H-1B1 Chile pathway and won't stall your offer letter waiting for legal team education.
Confirm your offer letter specifies full-time employment
The H-1B1 visa requires a legitimate U.S. job offer for a specific role at a named employer. Part-time offers, contractor arrangements, or vague consulting agreements won't satisfy consular review. Get a formal employment letter stating your title, duties, and start date.
Check that your employer's LCA wage meets prevailing wage
DOL requires the LCA to certify a wage at or above the prevailing level for your occupation and location. Before the offer stage, run your role's wage level through the OFLC Wage Search to know what Level I through IV rates look like for ML engineers in your target city.
Use your two-year H-1B1 renewal cycle strategically
Unlike H-1B visa, the H-1B1 Chile visa doesn't lead directly to a green card filing while in status. If your employer plans to sponsor permanent residence, discuss PERM timing early so the labor certification process starts before your first renewal cycle ends.
Frequently Asked Questions
Does an ML Engineer role qualify as a specialty occupation for the H-1B1 Chile visa?
Yes. ML Engineer roles require theoretical and practical application of machine learning, statistics, and software engineering, and typically demand a bachelor's degree or higher in computer science, mathematics, or a related field. USCIS and consular officers assess specialty occupation based on the degree requirement for the specific position, not the job title alone. Your offer letter and employer documentation must tie the role's duties to that degree requirement.
How does the H-1B1 Chile visa compare to the H-1B for ML Engineer jobs?
The H-1B1 Chile visa has a dedicated annual cap of 1,400 visas for Chilean nationals, no lottery, and consular processing rather than a USCIS petition, which means faster and more predictable access to U.S. ML roles. The H-1B requires USCIS approval, is subject to an 85,000-slot lottery with a roughly 25 percent selection rate, and carries higher employer filing costs. The trade-off is that the H-1B1 doesn't support dual intent, so long-term green card planning requires separate timing.
How do I find U.S. employers willing to sponsor an H-1B1 Chile visa for an ML Engineer?
Use Migrate Mate to search for employers with active Labor Condition Application filing history in machine learning and software engineering roles. Companies that have already filed LCAs for technical positions understand the sponsorship process and are far less likely to withdraw an offer once they learn the H-1B1 visa requires employer LCA filing.
What documents do I need for my H-1B1 Chile consular interview as an ML Engineer?
You'll need your certified LCA from DOL, a formal job offer letter from your employer, your academic credentials showing a degree in a directly related field, your DS-160 confirmation, and valid passport. Consular officers may also ask for evidence tying your degree field to ML engineering duties, so bring transcripts or a credential evaluation if your degree title isn't an obvious match for the role.
Can I work for multiple clients or on a project basis with an H-1B1 Chile visa as an ML Engineer?
The H-1B1 visa is employer-specific and tied to the petitioning company named on the LCA. Consulting arrangements where you bill multiple clients or work on-site at third-party locations raise compliance questions under DOL rules. If your role involves client-facing deployment of ML models, your employer's LCA must accurately reflect the actual worksite locations, and USCIS guidance on third-party placements applies to the underlying specialty occupation determination.