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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INTRODUCTION
As an MLOps Engineer, you will be the backbone of our machine learning infrastructure, ensuring that AI/ML systems are reliable, scalable, and continuously improving in production. You will bridge the gap between data science and engineering, driving operational excellence across the full ML lifecycle.
DESCRIPTION
The MLOps Engineer will drive end-to-end quality initiatives across data ingestion, model training, deployment pipelines, and MLOps tooling. This hire will build, deploy, and optimize AI/ML based applications with a strong emphasis on scalable, and production-ready systems. You will establish standard methodologies for model integration, deployment, and monitoring using CI/CD principles.
Responsibilities
- Explore, design, and implement advanced ML infrastructure frameworks and tools to accelerate model development and delivery.
- Champion model observability, incident response, prompt versioning, and feedback loops to ensure continuous model health and performance.
- Design and maintain automated pipelines for model training, evaluation, versioning, and deployment.
- Partner closely with ML Engineers and Data Scientists to define metrics, gather requirements, and deliver impactful solutions.
- Enforce model governance, validation standards, and best practices across teams to ensure reproducibility and compliance.
- Identify and resolve bottlenecks in ML workflows, improving system reliability, latency, and throughput at scale.
- Leverage AI coding assistants and LLM-based tools (e.g., Claude, Gemini, GitHub Copilot) to accelerate development, automate repetitive tasks, and improve engineering productivity across ML workflows.
- Use LLM-based tools to assist in drafting technical documentation, runbooks, and incident post-mortems, reducing operational overhead.
- Apply LLM assistants to support code reviews, test generation, and pipeline debugging to improve overall code quality and team velocity.
MINIMUM QUALIFICATIONS
- 8 years in software engineering with demonstrated experience in large-scale software system design and implementation.
- Bachelor's Degree in Software Engineering, Computer Science, Statistics, Data Mining, Machine Learning, Operations Research, or related field.
- Proven track record of shipping and maintaining production-grade ML systems end-to-end.
- Strong experience with distributed systems, databases (SQL/NoSQL), cloud platforms (AWS, Azure, or GCP), and container orchestration tools such as Kubernetes.
- Hands-on experience with MLOps tooling and platforms such as Ray, MLflow, Kubeflow, SageMaker, Vertex AI, or similar.
- Proficiency in Python and familiarity with ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
- Experience building and managing CI/CD pipelines for ML workflows using tools such as Jenkins, GitHub Actions, or ArgoCD.
- Strong understanding of data pipeline orchestration tools such as Airflow, Prefect, or similar.
PREFERRED QUALIFICATIONS
- 10 years of related experience building high-throughput, scalable applications or machine learning models in a production environment.
- Familiarity with model monitoring, drift detection, and observability practices in production environments.
- Excellent cross-functional communication skills with the ability to collaborate effectively across engineering and data science teams.
- Comfort using LLM-based tools such as Claude, Gemini, or ChatGPT to assist with code generation, documentation, debugging, and workflow automation.
- Demonstrated ability to critically evaluate and validate LLM-generated outputs, ensuring accuracy and reliability before applying them in production contexts.
- Experience incorporating AI-assisted tools into day-to-day engineering workflows, with an understanding of their limitations and appropriate use cases.
PAY & BENEFITS
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $212,000 and $318,400, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation.
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant.
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

INTRODUCTION
As an MLOps Engineer, you will be the backbone of our machine learning infrastructure, ensuring that AI/ML systems are reliable, scalable, and continuously improving in production. You will bridge the gap between data science and engineering, driving operational excellence across the full ML lifecycle.
DESCRIPTION
The MLOps Engineer will drive end-to-end quality initiatives across data ingestion, model training, deployment pipelines, and MLOps tooling. This hire will build, deploy, and optimize AI/ML based applications with a strong emphasis on scalable, and production-ready systems. You will establish standard methodologies for model integration, deployment, and monitoring using CI/CD principles.
Responsibilities
- Explore, design, and implement advanced ML infrastructure frameworks and tools to accelerate model development and delivery.
- Champion model observability, incident response, prompt versioning, and feedback loops to ensure continuous model health and performance.
- Design and maintain automated pipelines for model training, evaluation, versioning, and deployment.
- Partner closely with ML Engineers and Data Scientists to define metrics, gather requirements, and deliver impactful solutions.
- Enforce model governance, validation standards, and best practices across teams to ensure reproducibility and compliance.
- Identify and resolve bottlenecks in ML workflows, improving system reliability, latency, and throughput at scale.
- Leverage AI coding assistants and LLM-based tools (e.g., Claude, Gemini, GitHub Copilot) to accelerate development, automate repetitive tasks, and improve engineering productivity across ML workflows.
- Use LLM-based tools to assist in drafting technical documentation, runbooks, and incident post-mortems, reducing operational overhead.
- Apply LLM assistants to support code reviews, test generation, and pipeline debugging to improve overall code quality and team velocity.
MINIMUM QUALIFICATIONS
- 8 years in software engineering with demonstrated experience in large-scale software system design and implementation.
- Bachelor's Degree in Software Engineering, Computer Science, Statistics, Data Mining, Machine Learning, Operations Research, or related field.
- Proven track record of shipping and maintaining production-grade ML systems end-to-end.
- Strong experience with distributed systems, databases (SQL/NoSQL), cloud platforms (AWS, Azure, or GCP), and container orchestration tools such as Kubernetes.
- Hands-on experience with MLOps tooling and platforms such as Ray, MLflow, Kubeflow, SageMaker, Vertex AI, or similar.
- Proficiency in Python and familiarity with ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
- Experience building and managing CI/CD pipelines for ML workflows using tools such as Jenkins, GitHub Actions, or ArgoCD.
- Strong understanding of data pipeline orchestration tools such as Airflow, Prefect, or similar.
PREFERRED QUALIFICATIONS
- 10 years of related experience building high-throughput, scalable applications or machine learning models in a production environment.
- Familiarity with model monitoring, drift detection, and observability practices in production environments.
- Excellent cross-functional communication skills with the ability to collaborate effectively across engineering and data science teams.
- Comfort using LLM-based tools such as Claude, Gemini, or ChatGPT to assist with code generation, documentation, debugging, and workflow automation.
- Demonstrated ability to critically evaluate and validate LLM-generated outputs, ensuring accuracy and reliability before applying them in production contexts.
- Experience incorporating AI-assisted tools into day-to-day engineering workflows, with an understanding of their limitations and appropriate use cases.
PAY & BENEFITS
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $212,000 and $318,400, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation.
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant.
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
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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 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.
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Find Mlops Engineer JobsMlops Engineer E-3 Visa: 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.
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