H-1B Visa Machine Learning Engineer Jobs
Machine Learning Engineer roles qualify as H-1B visa specialty occupations under the computer-related occupations category, requiring at least a bachelor's degree in computer science, statistics, or a related field. Most ML positions fall under SOC code 15-2051 or 15-1212, which have strong H-1B filing histories at tech, finance, and healthcare companies.
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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 1,757+ H-1B Visa Machine Learning Engineer Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new H-1B Visa Machine Learning Engineer Jobs.
Get Access To All JobsTips for Finding H-1B Visa Sponsorship as a Machine Learning Engineer
Verify your SOC code before applying
ML Engineer roles get filed under different SOC codes depending on the employer. Check the O*NET profile for 15-2051 and 15-1212 to confirm your job duties align, since a SOC mismatch can trigger an RFE on specialty occupation grounds.
Target employers with certified LCA history
Filter employers by their DOL Labor Condition Application filings for ML-specific job titles before you apply. Migrate Mate surfaces this LCA data by occupation, so you can see which companies have filed for roles matching your background.
Document your degree equivalency early
If your degree is from outside the U.S. or in a field adjacent to ML, get a credential evaluation before your employer files. USCIS scrutinizes degree-to-role fit for ML positions, and a credential evaluation letter strengthens the specialty occupation argument.
Negotiate premium processing into your offer
Standard H-1B processing can run several months, which creates gaps if you're transitioning from OPT or another status. Ask during negotiations whether the employer will file with USCIS premium processing so your petition adjudicates within 15 business days.
Check prevailing wage level before accepting an offer
Your employer must certify your salary meets the DOL prevailing wage for your SOC code and work location. Use the OFLC Wage Search to look up Level I through Level IV wages for ML roles in your metro area before you sign.
Prepare for research exemption eligibility questions
ML Engineers at universities or nonprofit research organizations may qualify for cap-exempt H-1B sponsorship, bypassing the lottery entirely. Confirm whether your employer holds cap-exempt status with USCIS before assuming you need to compete in the annual lottery.
H-1B Visa Machine Learning Engineer: Frequently Asked Questions
Does a Machine Learning Engineer role qualify as an H-1B specialty occupation?
Yes. ML Engineer positions require at least a bachelor's degree in computer science, data science, statistics, or a closely related field, which meets the USCIS specialty occupation definition. Employers document this in the H-1B petition by tying the job duties, such as model development and algorithm design, to that specific degree requirement. Roles that accept any bachelor's degree regardless of field are harder to defend and more likely to receive an RFE.
Which SOC codes do employers use when filing H-1B petitions for ML Engineers?
Most employers file under SOC 15-2051 (Data Scientists) or SOC 15-1212 (Information Security Analysts), though some use 15-1299 (Computer Occupations, All Other) depending on how the job duties are framed. The SOC code affects the DOL prevailing wage tier and the strength of the specialty occupation argument. Confirm which code your employer intends to use before the LCA is filed, since changing it later requires refiling with DOL.
How do I find employers who have sponsored H-1B visas for Machine Learning Engineer jobs?
Search Migrate Mate to browse ML Engineer roles filtered by employers with verified H-1B LCA filing history. DOL LCA disclosure data is public and shows which companies have certified positions under ML-related SOC codes. Targeting employers with an established sponsorship track record reduces the risk of encountering a company unfamiliar with the H-1B process mid-hiring-cycle.
Can I transfer my H-1B to a new ML Engineer role without losing my place in the cap?
Yes. If you've already been counted against the H-1B cap, you can transfer your H-1B to a new employer through the H-1B portability provision under AC21, which lets you start work as soon as the new I-129 is filed, not after approval. Your new employer files a new LCA with DOL for the specific ML role and location, then petitions USCIS. You don't reenter the lottery.
What triggers an RFE on an H-1B petition for a Machine Learning Engineer position?
The two most common RFE triggers for ML Engineer petitions are specialty occupation disputes and degree-field mismatches. USCIS may question whether the role genuinely requires a specific bachelor's degree if the job description is broad or lists multiple qualifying fields. Employers strengthen the petition by providing industry wage data, internal job posting requirements, and documentation showing that ML roles across the industry require the same degree field.