E-3 Visa Mlops Engineer Jobs
MLOps Engineer roles qualify for E-3 visa sponsorship as specialty occupations requiring a bachelor's degree in computer science, data science, or a related field. The E-3 has no lottery and no annual cap, making it a reliable path for Australian engineers pursuing U.S. roles in machine learning infrastructure and model deployment.
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Get Access To All JobsINTRODUCTION
NVIDIA is seeking a Senior MLOps Engineer to join our DSX Enablement team, collaborating closely with strategic customers to implement and enhance groundbreaking AI workloads. We partner with the world's most innovative AI companies and open-source communities to address their most challenging technical problems.
What you will be doing:
In this role, you will develop innovative solutions that advance AI infrastructure capabilities, advise infrastructure experts on the demands of ML workloads, help practitioners diagnose and solve full-stack AI and ML system problems, and work on a team with direct responsibility for the success of internal and external customers’ AI and ML initiatives, including LLM performance evaluation and supporting new hardware in open-source frameworks. You will:
- Build and deploy custom AI solutions on NeoCloud platforms and NVIDIA Cloud Partners (NCPs), including distributed training, inference optimization, and MLOps pipelines,
- Act as a primary technical contact for internal and external customers and partners, guiding joint engagements, ensuring the success of initiatives on DGX Cloud, and solving complex problems in production,
- Work closely with the teams building the infrastructure software and accelerated frameworks that support today’s most compelling AI applications,
- Profile and tune large-scale training and inference workloads on NCP platforms, leading efforts to reduce latency, cost, and operational risk, and
- Develop open-source tools and reference architectures to make it easier to build and manage machine learning and AI workloads, pipelines, and systems at scale.
What we need to see:
- BS, MS, or Ph.D. in Computer Science, Computer/Electrical Engineering, or a related technical field, or equivalent experience.
- 8+ years of experience in technical roles such as data science, data engineering, or ML engineering, ideally targeting large-scale production systems.
- Demonstrated AI/ML experience across multiple phases of the machine learning lifecycle, from exploratory analysis to production systems.
- Facility with systems topics including Linux, batch schedulers, Kubernetes, distributed filesystems, and advanced networking at datacenter scale.
- Solid scripting and programming skills in languages like bash and Python and solid systems programming skills in a language like C++, Go, or Rust.
- Experience using machine learning or deep learning frameworks for training and inference.
- Excellent communication and technical presentation skills, with the ability to clearly articulate architectures, trade-offs, and recommendations to both engineering and leadership audiences.
- A clear record of engineering discipline and execution on interesting projects, whether you’re working alone or collaborating on a team.
Ways to stand out from the crowd:
- Experience contributing to and working in open-source communities.
- Experience with the NVIDIA ecosystem, including DGX systems, CUDA, NeMo, RAPIDS, Triton, NIM, and NVIDIA networking technologies such as InfiniBand, NVLink, and RoCE.
- Experience and familiarity building machine learning systems in a security-critical environment and distributed training and inference frameworks.
- Familiarity with MLOps practices in a cloud-native context: containerization, CI/CD pipelines, workflow automation, observability stacks, and GitOps workflows.
- Direct experience drawing on deep systems knowledge to diagnose and fix performance or correctness problems that span multiple layers of the application stack, like hardware, networking, accelerator, hypervisor or OS, compilers or runtimes, application code, and libraries.
NVIDIA offers competitive salaries and a generous benefits package. It is recognized as one of the technology world’s most desirable employers. We have some of the most innovative and dedicated people working here. Due to rapid growth, our outstanding teams are expanding quickly. Join us to make a lasting impact on the world!
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until August 24, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
INTRODUCTION
NVIDIA is seeking a Senior MLOps Engineer to join our DSX Enablement team, collaborating closely with strategic customers to implement and enhance groundbreaking AI workloads. We partner with the world's most innovative AI companies and open-source communities to address their most challenging technical problems.
What you will be doing:
In this role, you will develop innovative solutions that advance AI infrastructure capabilities, advise infrastructure experts on the demands of ML workloads, help practitioners diagnose and solve full-stack AI and ML system problems, and work on a team with direct responsibility for the success of internal and external customers’ AI and ML initiatives, including LLM performance evaluation and supporting new hardware in open-source frameworks. You will:
- Build and deploy custom AI solutions on NeoCloud platforms and NVIDIA Cloud Partners (NCPs), including distributed training, inference optimization, and MLOps pipelines,
- Act as a primary technical contact for internal and external customers and partners, guiding joint engagements, ensuring the success of initiatives on DGX Cloud, and solving complex problems in production,
- Work closely with the teams building the infrastructure software and accelerated frameworks that support today’s most compelling AI applications,
- Profile and tune large-scale training and inference workloads on NCP platforms, leading efforts to reduce latency, cost, and operational risk, and
- Develop open-source tools and reference architectures to make it easier to build and manage machine learning and AI workloads, pipelines, and systems at scale.
What we need to see:
- BS, MS, or Ph.D. in Computer Science, Computer/Electrical Engineering, or a related technical field, or equivalent experience.
- 8+ years of experience in technical roles such as data science, data engineering, or ML engineering, ideally targeting large-scale production systems.
- Demonstrated AI/ML experience across multiple phases of the machine learning lifecycle, from exploratory analysis to production systems.
- Facility with systems topics including Linux, batch schedulers, Kubernetes, distributed filesystems, and advanced networking at datacenter scale.
- Solid scripting and programming skills in languages like bash and Python and solid systems programming skills in a language like C++, Go, or Rust.
- Experience using machine learning or deep learning frameworks for training and inference.
- Excellent communication and technical presentation skills, with the ability to clearly articulate architectures, trade-offs, and recommendations to both engineering and leadership audiences.
- A clear record of engineering discipline and execution on interesting projects, whether you’re working alone or collaborating on a team.
Ways to stand out from the crowd:
- Experience contributing to and working in open-source communities.
- Experience with the NVIDIA ecosystem, including DGX systems, CUDA, NeMo, RAPIDS, Triton, NIM, and NVIDIA networking technologies such as InfiniBand, NVLink, and RoCE.
- Experience and familiarity building machine learning systems in a security-critical environment and distributed training and inference frameworks.
- Familiarity with MLOps practices in a cloud-native context: containerization, CI/CD pipelines, workflow automation, observability stacks, and GitOps workflows.
- Direct experience drawing on deep systems knowledge to diagnose and fix performance or correctness problems that span multiple layers of the application stack, like hardware, networking, accelerator, hypervisor or OS, compilers or runtimes, application code, and libraries.
NVIDIA offers competitive salaries and a generous benefits package. It is recognized as one of the technology world’s most desirable employers. We have some of the most innovative and dedicated people working here. Due to rapid growth, our outstanding teams are expanding quickly. Join us to make a lasting impact on the world!
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until August 24, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
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Get Access To All JobsTips for Finding E-3 Visa Sponsorship as a Mlops Engineer
Frame your degree for specialty occupation
E-3 visa eligibility hinges on your degree matching the role. A computer science or data engineering degree maps cleanly, but if yours is in a related field like mathematics or statistics, document how your coursework and experience directly support MLOps work.
Target employers with active ML infrastructure teams
Companies running large-scale model deployment pipelines are most likely to understand E-3 sponsorship requirements. Focus your search on organizations with dedicated platform or ML engineering teams, not those where MLOps is a secondary responsibility shared across roles.
Clarify your Australian citizenship early in screening
Many U.S. recruiters conflate visa sponsorship with the H-1B lottery and assume it takes months. Tell them upfront that your E-3 requires no lottery, no cap, and can be processed in weeks. This removes a common reason offers get stalled or withdrawn.
Confirm the job description specifies a required degree
The DOL requires your LCA job posting to list a specific degree as required, not preferred. If the role description says 'bachelor's preferred,' ask HR to revise it before the LCA is filed. A 'preferred' qualification can undermine your specialty occupation determination.
Use Migrate Mate's E-3 filing service after your offer
Once you have a signed offer, use Migrate Mate's E-3 filing service to handle your LCA and visa paperwork. The service manages DOL certification, DS-160 preparation, and consulate appointment coordination so you and your employer aren't navigating the process alone.
Check E-Verify enrollment before your start date
If your MLOps role involves handling certain federal contracts or sensitive data systems, your employer may be required to be enrolled in E-Verify. Confirm enrollment status before you accept, as unenrolled employers may face onboarding delays that affect your start date.
E-3 Visa Mlops Engineer: Frequently Asked Questions
How do I find MLOps Engineer jobs with E-3 visa sponsorship?
Migrate Mate is built specifically for this search. It filters MLOps Engineer roles by E-3 sponsorship readiness, so you're not sifting through listings from employers unfamiliar with the visa. Standard job searches surface roles without any indication of whether the hiring team understands E-3 requirements or has sponsored Australians before.
How much does it cost to get an E-3 visa?
Migrate Mate's E-3 filing service covers the entire process for $499, including the Labor Condition Application, visa document preparation, and consulate appointment guidance. Traditional immigration lawyers charge $2,000–$5,000+ for the same work. The E-3 has less paperwork than most work visas, so paying thousands for legal help is usually unnecessary.
Does an MLOps Engineer role qualify as a specialty occupation for E-3?
Yes. MLOps Engineer roles require a theoretical and practical application of computer science, systems engineering, or data engineering, and typically require at least a bachelor's degree in a relevant field. USCIS evaluates specialty occupation on a case-by-case basis, but roles involving model lifecycle management, CI/CD pipeline design, and infrastructure-as-code are well-supported by the definition.
How does the E-3 compare to H-1B for MLOps Engineers in the United States?
For Australian MLOps Engineers, the E-3 is significantly more predictable. The H-1B is subject to an annual lottery with a roughly 25% selection rate, meaning most registrants are not selected. The E-3 has a 10,500 annual cap that has never been exhausted, requires no lottery, and can typically be processed within weeks of a job offer. You can start work after consulate approval without waiting for a fiscal year start date.
Can I switch MLOps Engineer employers while on an E-3 visa?
Yes, but you need a new E-3 for each employer. Your E-3 is employer-specific, so if you change roles or companies, your new employer must file a fresh LCA with the DOL and you'll need to attend a new consulate appointment. There's no portability provision like some other visa categories, so plan for a processing gap between your last day and your new start date.