Principal Machine Learning Engineer Jobs in USA with Visa Sponsorship
Principal Machine Learning Engineer roles sit firmly within H-1B visa specialty occupation territory, requiring a directly related degree and employer-filed Labor Condition Application. Big Tech, AI labs, and growth-stage startups all sponsor this title regularly. The seniority level often supports EB-2 or EB-3 green card petitions alongside your nonimmigrant status.
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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 504+ Principal Machine Learning Engineer Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Principal Machine Learning Engineer roles.
Get Access To All JobsTips for Finding Visa Sponsorship as a Principal Machine Learning Engineer
Align your degree field to the role
USCIS scrutinizes whether your degree is in a directly related field, not just a technical field. Computer Science, Statistics, or Electrical Engineering maps cleanly. A degree in a loosely related discipline may require an advisory opinion letter from a credentials evaluator before filing.
Verify prevailing wage tier before negotiating
Your employer's LCA must certify a wage at or above the DOL prevailing wage for your location and level. Use the OFLC Wage Search to confirm the Level III or Level IV wage for your metro area before you finalize any offer discussion.
Target employers with active LCA filing history
Use Migrate Mate to find companies with verified LCA filings for Principal Machine Learning Engineer roles, filtered by city. This tells you which employers have already navigated the sponsorship process for this title, not just those who claim they sponsor.
Flag the O*NET profile during credential review
O*NET classifies this role in Job Zone 5, the highest complexity tier, requiring a master's or doctoral degree for most positions. Referencing this in your application materials strengthens the specialty occupation argument your employer must make to USCIS.
Push for concurrent EB-2 filing at the offer stage
At the principal level, many employers can initiate PERM labor certification alongside your H-1B petition. Getting this conversation into the offer stage rather than after your first year protects you against priority date backlogs, especially if you're from India or China.
Anticipate the H-1B cap and plan your timeline
H-1B is subject to an annual cap and selection process, with rules that have changed recently. If you're not already in H-1B status, structure your job search so an offer can be in place well before the registration window opens each year.
Frequently Asked Questions
Does a Principal Machine Learning Engineer role qualify as a specialty occupation for H-1B?
Yes. The role requires deep knowledge of machine learning frameworks, statistical modeling, and system design, all of which demand at minimum a bachelor's degree in a directly related field such as Computer Science, Mathematics, or Electrical Engineering. USCIS evaluates the degree requirement against the specific duties in the job description, so your employer's petition language matters as much as your credentials.
Is a master's degree required, or will a bachelor's degree support sponsorship?
A bachelor's degree in a directly related field can technically qualify, but most employers filing for principal-level roles set a master's or doctoral degree as the minimum requirement. This higher degree threshold also opens eligibility for the H-1B advanced degree cap, giving you an additional selection pool, and strengthens an EB-2 green card petition if your employer pursues one.
Which visa types realistically apply to Principal Machine Learning Engineers?
The H-1B is the primary path. If you hold Australian citizenship, the E-3 visa offers a faster, cap-exempt alternative with an LCA filed by the employer and no lottery. Chilean and Singaporean nationals may qualify for the H-1B1 visa. All three require the employer to file an LCA with the DOL certifying the prevailing wage. For permanent residence, EB-2 or EB-3 are the standard green card categories at this level.
How do I find employers actively sponsoring Principal Machine Learning Engineer roles?
Migrate Mate lists employers with verified LCA sponsorship history for machine learning roles, searchable by city and seniority. This is the most direct way to identify companies that have already filed for this title rather than relying on job postings that may or may not lead to sponsorship.
Can my employer start the green card process while I'm on H-1B?
Yes, and at the principal level it's worth raising this during the offer stage. Your employer can initiate PERM labor certification and file an I-140 petition concurrently with or after your H-1B approval. If your priority date is current, you can file for adjustment of status while maintaining H-1B status. For India and China-born applicants, priority date backlogs make starting this process early a practical necessity.
What is the prevailing wage requirement for sponsored Principal Machine Learning Engineer jobs?
U.S. employers sponsoring a visa must pay at least the prevailing wage, which is what workers in the same role, area, and experience level typically earn. The Department of Labor sets this rate to make sure companies aren't hiring foreign workers simply because they'd accept lower pay than a U.S. worker. It varies by job title, location, and experience. You can look up current prevailing wage rates for any occupation and location using the OFLC Wage Search page.