E-3 Visa AI ML Engineering Jobs
AI ML Engineering roles in the U.S. qualify as E-3 visa specialty occupations, making them a strong fit for Australian professionals seeking sponsorship. The E-3 has no lottery and no annual cap, so you can apply as soon as you have a qualifying offer from a U.S. employer willing to file a Labor Condition Application.
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Get Access To All JobsINTRODUCTION
NVIDIA is redefining the automotive industry through accelerated computing, artificial intelligence, simulation, and full-stack autonomous vehicle development. The pace and quality of AV development depend on delivering the right sensor, ground-truth, and derived data to machine learning teams reliably, transparently, and at scale. It's truly the data that makes the cars drive!
The AV MLOps Dataset Release team transforms large-scale automotive data into versioned, trustworthy datasets used to train and evaluate machine learning models across the autonomous-driving stack. We are seeking an ML Data Operations Lead to own the customer-facing operational lifecycle of these releases. In this role, you will work at the intersection of machine learning, data engineering, infrastructure, and release operations. You will partner with ML engineers to understand their data needs, translate those needs into actionable release requirements, coordinate execution with the engineering team, and ensure every release is delivered with clear validation, documentation, and communication. This is a senior individual-contributor role. It requires sufficient technical depth to investigate problems, assess delivery risk, and challenge unclear requirements, while focusing primarily on operational ownership rather than developing the underlying data pipelines.
ROLE AND RESPONSIBILITIES:
- Serve as the primary operational partner for ML engineers and other internal consumers of AV datasets.
- Capture and clarify dataset release requirements, including intended use cases, required signals and labels, data volumes, release cadence, delivery timelines, storage destinations, and acceptance criteria.
- Be responsible for the release calendar and coordinate priorities, dependencies, engineering readiness, and compute capacity across multiple concurrent dataset-release tracks.
- Monitor production release workflows from launch through delivery. Identify failures, stalled tasks, resource constraints, missing data, and other risks, then bring together the appropriate engineers and infrastructure owners to drive resolution.
- Validate release results against expected volumes, signals, versions, and quality criteria before communicating availability to customers.
- Maintain timely, accurate communication with customers regarding release status, risks, incidents, changing estimates, and recovery plans.
- Produce release notes, delivery announcements, known-issue documentation, and handoff information that enable ML teams to understand and use each dataset confidently.
BASIC QUALIFICATIONS:
- Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or a related field, or equivalent experience.
- 6+ years of experience in ML data operations, technical service delivery, dataset operations, release operations, technical program execution, or another data-intensive operational role.
- Solid understanding of the machine learning data lifecycle, including data collection, curation, labeling, validation, versioning, release, storage, and consumption by training or evaluation pipelines.
- Ability to use SQL and data-analysis tools to investigate dataset contents, reconcile expected and delivered results, and identify quality or completeness issues.
- Strong customer orientation and skill in translating between ML engineers, data specialists, infrastructure teams, and other technical collaborators.
- Excellent written communication skills, including the ability to produce detailed requirements, release notes, status updates, incident summaries, and operating procedures.
- Excellent judgment when balancing customer timelines, engineering capacity, system reliability, data quality, and competing release priorities.
- Proven track record of influencing without direct authority and driving work to completion across a highly matrixed organization.
- Comfort operating in a fast-moving environment where requirements, data availability, and technical constraints may change quickly.
PREFERRED QUALIFICATIONS:
- Experience operating large-scale dataset generation, materialization, validation, or delivery workflows, especially for autonomous-driving, ADAS, robotics, or computer-vision systems.
- Familiarity with automotive sensor and ground-truth data, including camera, lidar, radar, mapping, calibration, or multimodal datasets.
- Hands-on experience with Python, notebooks, Databricks, dashboards, or lightweight automation used to investigate data and improve operational workflows.
- Experience defining service-level objectives, operational metrics, alerting, incident-management practices, and root-cause corrective actions.
- A track record of converting frequently repeated customer requests or operational problems into standardized, automated, and scalable services.
NVIDIA brings together some of the most skilled and creative people in technology to solve problems that were once considered impossible. You will have the opportunity to work with teams advancing autonomous vehicles, artificial intelligence, accelerated computing, and large-scale data systems while directly improving the speed and reliability of machine learning development.
COMPENSATION
- Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 168,000 USD - 258,750 USD for Level 4, and 200,000 USD - 322,000 USD for Level 5.
- You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until September 14, 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 redefining the automotive industry through accelerated computing, artificial intelligence, simulation, and full-stack autonomous vehicle development. The pace and quality of AV development depend on delivering the right sensor, ground-truth, and derived data to machine learning teams reliably, transparently, and at scale. It's truly the data that makes the cars drive!
The AV MLOps Dataset Release team transforms large-scale automotive data into versioned, trustworthy datasets used to train and evaluate machine learning models across the autonomous-driving stack. We are seeking an ML Data Operations Lead to own the customer-facing operational lifecycle of these releases. In this role, you will work at the intersection of machine learning, data engineering, infrastructure, and release operations. You will partner with ML engineers to understand their data needs, translate those needs into actionable release requirements, coordinate execution with the engineering team, and ensure every release is delivered with clear validation, documentation, and communication. This is a senior individual-contributor role. It requires sufficient technical depth to investigate problems, assess delivery risk, and challenge unclear requirements, while focusing primarily on operational ownership rather than developing the underlying data pipelines.
ROLE AND RESPONSIBILITIES:
- Serve as the primary operational partner for ML engineers and other internal consumers of AV datasets.
- Capture and clarify dataset release requirements, including intended use cases, required signals and labels, data volumes, release cadence, delivery timelines, storage destinations, and acceptance criteria.
- Be responsible for the release calendar and coordinate priorities, dependencies, engineering readiness, and compute capacity across multiple concurrent dataset-release tracks.
- Monitor production release workflows from launch through delivery. Identify failures, stalled tasks, resource constraints, missing data, and other risks, then bring together the appropriate engineers and infrastructure owners to drive resolution.
- Validate release results against expected volumes, signals, versions, and quality criteria before communicating availability to customers.
- Maintain timely, accurate communication with customers regarding release status, risks, incidents, changing estimates, and recovery plans.
- Produce release notes, delivery announcements, known-issue documentation, and handoff information that enable ML teams to understand and use each dataset confidently.
BASIC QUALIFICATIONS:
- Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or a related field, or equivalent experience.
- 6+ years of experience in ML data operations, technical service delivery, dataset operations, release operations, technical program execution, or another data-intensive operational role.
- Solid understanding of the machine learning data lifecycle, including data collection, curation, labeling, validation, versioning, release, storage, and consumption by training or evaluation pipelines.
- Ability to use SQL and data-analysis tools to investigate dataset contents, reconcile expected and delivered results, and identify quality or completeness issues.
- Strong customer orientation and skill in translating between ML engineers, data specialists, infrastructure teams, and other technical collaborators.
- Excellent written communication skills, including the ability to produce detailed requirements, release notes, status updates, incident summaries, and operating procedures.
- Excellent judgment when balancing customer timelines, engineering capacity, system reliability, data quality, and competing release priorities.
- Proven track record of influencing without direct authority and driving work to completion across a highly matrixed organization.
- Comfort operating in a fast-moving environment where requirements, data availability, and technical constraints may change quickly.
PREFERRED QUALIFICATIONS:
- Experience operating large-scale dataset generation, materialization, validation, or delivery workflows, especially for autonomous-driving, ADAS, robotics, or computer-vision systems.
- Familiarity with automotive sensor and ground-truth data, including camera, lidar, radar, mapping, calibration, or multimodal datasets.
- Hands-on experience with Python, notebooks, Databricks, dashboards, or lightweight automation used to investigate data and improve operational workflows.
- Experience defining service-level objectives, operational metrics, alerting, incident-management practices, and root-cause corrective actions.
- A track record of converting frequently repeated customer requests or operational problems into standardized, automated, and scalable services.
NVIDIA brings together some of the most skilled and creative people in technology to solve problems that were once considered impossible. You will have the opportunity to work with teams advancing autonomous vehicles, artificial intelligence, accelerated computing, and large-scale data systems while directly improving the speed and reliability of machine learning development.
COMPENSATION
- Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 168,000 USD - 258,750 USD for Level 4, and 200,000 USD - 322,000 USD for Level 5.
- You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until September 14, 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.
See all 1,183+ E-3 Visa AI ML Engineering Jobs
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Get Access To All JobsTips for Finding E-3 Visa Sponsorship in AI ML Engineering
Frame your credentials for U.S. specialty occupation
Your Australian three-year bachelor's degree in computer science, data science, or a related field satisfies the E-3 degree requirement. Get a credential evaluation before you apply so employers aren't left guessing about equivalency.
Target employers with active LCA filing history
Search DOL's FLAG portal for companies that have filed Labor Condition Applications for AI or ML job titles. Prior LCA filings signal that a hiring team already understands the E-3 process and won't stall at the sponsorship conversation.
Raise E-3 sponsorship early in final-round interviews
Many U.S. tech hiring managers confuse E-3 with H-1B visa and assume a lottery is involved. Clarify that E-3 requires only an LCA and a consulate appointment, with no cap or lottery, before you reach the offer stage.
Ensure your job description matches your degree field
For AI ML Engineering roles, the position must require a relevant technical degree, not just 'a bachelor's in any field.' If the job description is vague, ask HR to revise it before the LCA is filed to avoid a DOL denial.
Use Migrate Mate's E-3 filing service to manage your LCA
Once you have an offer, use Migrate Mate's E-3 filing service to handle your LCA and visa paperwork end-to-end. This is especially useful when your employer's legal team has no prior E-3 experience and needs a structured process.
Book your consulate appointment before resigning overseas
E-3 visas are issued at Australian consulates, not through USCIS. Check current appointment availability at Sydney, Melbourne, or Perth before giving notice at your current job, since wait times vary by location and season.
E-3 Visa AI ML Engineering: Frequently Asked Questions
How do I find AI ML Engineering jobs with E-3 visa sponsorship?
Migrate Mate is built specifically for this search. It filters roles by E-3 sponsorship eligibility and surfaces employers with a history of filing Labor Condition Applications for technical positions. Searching general job boards for 'visa sponsorship' rarely filters for E-3 specifically, so you end up reviewing roles that only accommodate H-1B candidates.
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 AI ML Engineering qualify as an E-3 specialty occupation?
Yes, provided the role requires a bachelor's degree or higher in a directly related field such as computer science, machine learning, data science, or software engineering. Roles framed as 'nice to have a degree' rather than 'degree required' can fail the specialty occupation test, so the job description wording matters before your employer files the LCA with DOL.
How does the E-3 compare to the H-1B for AI ML Engineering roles?
For Australian nationals, the E-3 is significantly more practical for AI ML Engineering. There's no lottery, no annual cap, and no registration window to miss. The H-1B requires entering a randomized lottery with roughly a 25% selection rate, meaning you could go unselected for multiple years. E-3 applications are processed at the consulate and can be completed in weeks once the LCA is certified.
Can I switch AI ML Engineering employers while on an E-3 visa?
Yes, but you need a new LCA and a new visa stamp or change of status approval before you start with the new employer. You can't simply port your E-3 the way some H-1B holders port under portability rules. If you're already in the U.S., your new employer files a fresh LCA with DOL, and you either return to Australia for a new stamp or file a change of status with USCIS.