E-3 Visa Machine Learning Scientist Jobs
Machine Learning Scientist roles qualify for E-3 visa sponsorship as specialty occupations requiring at least a bachelor's degree in computer science, statistics, or a related field. The E-3 has no lottery and no annual cap, so Australian nationals can pursue U.S. positions year-round without competing for limited slots the way H-1B visa applicants do.
Find E-3 Visa Machine Learning Scientist JobsOverview
Showing 5 of 1,184+ Machine Learning Scientist jobs
See all 1,184+ Machine Learning Scientist Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Machine Learning Scientist roles.
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 1,184+ E-3 Visa Machine Learning Scientist Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new E-3 Visa Machine Learning Scientist Jobs.
Get Access To All JobsTips for Finding E-3 Visa Sponsorship as a Machine Learning Scientist
Frame your research portfolio for U.S. specialty occupation standards
DOL requires your role to meet specialty occupation criteria, meaning a specific degree field must be normal for the position. Document how your ML research, publications, or graduate work maps to a defined technical discipline, not just general data science experience.
Target employers with active LCA filing history in ML
Companies that have previously certified LCAs for machine learning or AI research roles already understand the E-3 visa process. Searching DOL's Foreign Labor Certification disclosure data by SOC code 15-2051 surfaces employers who have filed for similar positions before.
Find Machine Learning Scientist roles using Migrate Mate
Migrate Mate filters job listings by E-3 sponsorship eligibility, so you're not cold-applying to employers unfamiliar with Australian visa requirements. Use Migrate Mate's E-3 filing service to handle your LCA and visa paperwork once you have an offer.
Clarify your employer's LCA wage level before signing
DOL prevailing wage determinations for ML Scientists vary significantly by level. Confirm that your offered wage meets at least Level II requirements for your metro area before your employer submits the LCA, since a deficient wage certification will block your visa application.
Distinguish your PhD or honours research from general engineering work
Consular officers assess whether the offered role genuinely requires a specialist degree. If your background is research-heavy, bring documentation of published work, conference presentations, or specialised methods that separate the position from a standard software engineering role.
Time your consulate appointment around your employer's LCA certification
The E-3 consular application cannot proceed until your employer's LCA is certified by DOL, which currently averages around seven business days. Build that window into your start date negotiation so your visa appointment isn't scheduled before the certified LCA is in hand.
E-3 Visa Machine Learning Scientist: Frequently Asked Questions
How do I find Machine Learning Scientist jobs with E-3 visa sponsorship?
Migrate Mate is the recommended tool for this search. It surfaces Machine Learning Scientist roles at employers who are open to E-3 sponsorship, saving you from applying to companies unfamiliar with the visa. Because the E-3 has no lottery and no annual cap, any role that qualifies as a specialty occupation can be filled year-round, which expands the pool of willing sponsors compared to H-1B hiring.
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 Scientist role qualify as a specialty occupation for the E-3?
Yes, provided the position normally requires at least a bachelor's degree in a specific technical field such as computer science, statistics, applied mathematics, or a closely related discipline. Roles that accept any degree regardless of field, or that treat the degree as a general credential rather than a job-specific requirement, may not satisfy DOL's specialty occupation standard. Documenting the technical degree requirement in the job description strengthens the LCA.
How does the E-3 compare to the H-1B for Machine Learning Scientist positions?
The E-3 is significantly more predictable for Australian ML Scientists. The H-1B is subject to an annual cap and a random lottery, meaning a qualifying offer and petition can still be rejected by chance. The E-3 has a 10,500-slot annual allocation that has never been exhausted, so there is no lottery. Approved E-3 status lasts two years and renews indefinitely, and you can apply at the consulate within weeks of receiving an offer rather than waiting for an October start date.
Can I transfer my E-3 status if I move between machine learning roles or employers?
Yes, but each new employer must file a fresh LCA and your role must independently qualify as a specialty occupation. There is no portability rule that carries over from a previous E-3 employer the way AC21 works for H-1B holders. You should avoid a gap in authorised employment, and many practitioners recommend not resigning until the new LCA is certified and your visa documentation is updated.