H-1B1 Chile Visa Machine Learning Engineer Jobs
Machine Learning Engineer roles qualify for H-1B1 Chile visa sponsorship as specialty occupations requiring at least a bachelor's degree in computer science, engineering, or a related field. Chilean nationals skip the H-1B lottery entirely, with the 1,400-visa annual cap rarely exhausted and applications handled directly at the consulate.
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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 Machine Learning Engineer Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new H-1B1 Chile Visa Machine Learning Engineer Jobs.
Get Access To All JobsTips for Finding Visa Sponsorship as a Machine Learning Engineer
Verify your degree maps to the role
Consular officers assess whether your bachelor's degree field directly relates to machine learning engineering. A degree in computer science, mathematics, or electrical engineering clears the bar. Statistics or physics degrees may need supporting coursework documentation to establish the connection.
Target employers with active LCA filing history
Search the OFLC Wage Search to confirm a company has previously filed Labor Condition Applications for machine learning or software engineering roles. Employers already familiar with LCA certification are far less likely to stall when you raise H-1B1 Chile sponsorship in negotiations.
Use Migrate Mate to surface H-1B1 Chile employers
Filter your job search on Migrate Mate to find employers with documented H-1B1 Chile filing history for machine learning and engineering roles, so you're spending time on companies that have already worked through the sponsorship process rather than educating employers from scratch.
Benchmark your offer against DOL prevailing wage
Before signing an offer, run your job title and location through the OFLC Wage Search to confirm the offered salary meets the DOL prevailing wage for your wage level. An LCA will be rejected if the offered wage falls below the certified threshold, which delays your consular appointment.
Clarify the LCA timeline before accepting an offer
Ask the employer specifically how long DOL LCA certification typically takes on their end. Standard certification runs about seven business days, but employers filing for the first time may need additional lead time to register in the FLAG system and prepare supporting documentation.
Pull your O*NET occupation profile before interviews
Review the O*NET profile for Machine Learning Engineer or the closest matching occupation to understand the official degree requirements and job duties the consular officer will reference. Framing your experience using language aligned with O*NET strengthens your specialty occupation argument during the interview.
Frequently Asked Questions
Does a machine learning engineer role qualify as a specialty occupation for the H-1B1 Chile visa?
Yes. Machine learning engineering requires at least a bachelor's degree in computer science, mathematics, engineering, or a closely related field, which satisfies the specialty occupation definition. You'll want to ensure your offer letter and any employer documentation describe duties that require that theoretical and practical application of those disciplines, not just general software development tasks.
How does the H-1B1 Chile visa differ from H-1B for machine learning engineers?
The H-1B1 Chile visa has no lottery, a dedicated annual cap of 1,400 for Chilean nationals that rarely fills, and is processed at the consulate rather than through USCIS petition. You don't need an I-129 petition, which removes several months of waiting. The trade-off is that the H-1B1 does not allow dual intent, so you cannot simultaneously pursue a green card while on H-1B1 status.
Can I find machine learning engineer employers who sponsor H-1B1 Chile visas through Migrate Mate?
Yes. Migrate Mate lets you filter specifically for employers with H-1B1 Chile filing history in engineering and machine learning roles, so you're targeting companies that have already worked through the LCA and consular process rather than approaching employers who have no experience with this visa category.
What does the employer actually file for an H-1B1 Chile visa sponsorship?
The employer files a Labor Condition Application with the DOL through the FLAG system. DOL must certify the LCA, which typically takes about seven business days. Once certified, you take that LCA along with your job offer and supporting credentials to your consular interview. There's no USCIS petition stage, which is the key procedural difference from the H-1B process.
Can I renew my H-1B1 Chile visa if my machine learning engineering project extends beyond the initial period?
H-1B1 Chile status is granted in one-year increments and can be renewed indefinitely as long as you maintain a qualifying job offer and your employer files a new certified LCA for each renewal period. There's no statutory maximum on renewals, so long-term employment at the same company is straightforward provided the role continues to meet specialty occupation requirements.