E-3 Visa Machine Learning Research Jobs
Machine Learning Research roles qualify as E-3 specialty occupations, making Australian professionals eligible for U.S. employer sponsorship without entering a lottery. The E-3 visa is renewable indefinitely in two-year increments, so you can build a long-term research career at U.S. labs, universities, and tech companies without the H-1B visa cap pressure.
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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 1,193+ E-3 Visa Machine Learning Research Jobs
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Get Access To All JobsTips for Finding E-3 Visa Sponsorship in Machine Learning Research
Frame your research credentials for U.S. employers
Australian PhD and honours degrees translate directly to U.S. specialty occupation standards, but your CV needs to show publication records, conference presentations, and model deployment experience in terms U.S. hiring committees recognise.
Target employers with active DOL LCA filings
Search the DOL's Office of Foreign Labor Certification disclosure data for companies that have filed LCAs for ML research titles. This confirms an employer already understands the E-3 visa process and has sponsored similar roles before.
Clarify the specialty occupation case before accepting an offer
Not every ML title passes the specialty occupation test. Roles framed as general data analysis or software engineering may not qualify. Confirm the job description explicitly requires a degree in computer science, statistics, or a directly related field before you sign.
Use Migrate Mate's E-3 filing service for LCA and consulate prep
Once you have an offer, use Migrate Mate's E-3 filing service to handle your LCA and visa paperwork end-to-end. The LCA must be DOL-certified before your consulate appointment, and timing errors here are the most common cause of delays.
Find ML Research jobs on Migrate Mate
Migrate Mate filters job listings by E-3 sponsorship availability, so you're only seeing employers already set up to hire Australians. Search by ML Research job title to surface active roles without wading through listings that will reject your visa status.
Negotiate your start date around E-3 consulate wait times
Sydney, Melbourne, and Perth consulates have different appointment availability. Build at least four to six weeks between your signed offer and your proposed start date so your employer's LCA certification and your consulate slot align without forcing a rushed rescheduling.
E-3 Visa Machine Learning Research: Frequently Asked Questions
How do I find Machine Learning Research jobs that offer E-3 visa sponsorship?
Migrate Mate is built specifically for this search. It surfaces Machine Learning Research roles from employers already willing to sponsor Australian nationals on an E-3 visa, so you're not filtering out irrelevant listings manually. Because the E-3 has no lottery and relatively low employer cost compared to H-1B, more research-focused employers are open to it than you might expect.
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 a Machine Learning Research role qualify as a specialty occupation for E-3 purposes?
Most ML Research positions do qualify, provided the job description requires a bachelor's degree or higher in a specific field such as computer science, machine learning, statistics, or applied mathematics. Roles that only list a degree as preferred rather than required, or that accept any field of study, can fail the specialty occupation test. Review the job description carefully before applying.
How does the E-3 visa compare to H-1B for Machine Learning Research roles?
The E-3 has a 10,500 annual cap that has never been reached, so there's no lottery and no random selection risk. H-1B requires entering an annual lottery where selection is not guaranteed. For ML Research roles, both visas require the employer to file an LCA with DOL and demonstrate prevailing wage compliance, but the E-3 timeline is significantly more predictable.
Can I switch ML Research employers while on an E-3 visa?
Yes, but each new employer must file a fresh LCA with the DOL and you'll need a new E-3 visa stamp before re-entering the U.S. if you travel internationally. If you remain inside the U.S. and your current E-3 status is still valid, you can begin work for a new employer while their LCA is pending, though specific timing rules apply.