E-3 Visa Machine Learning Intern Jobs
Machine Learning Intern roles in the U.S. qualify for E-3 visa sponsorship when the position requires a bachelor's degree or higher in computer science, data science, or a related field. The E-3 has no lottery and no annual cap, making it a practical path for Australian graduates and early-career professionals pursuing ML work in the United States.
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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 1,184+ E-3 Visa Machine Learning Intern Jobs
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Get Access To All JobsTips for Finding E-3 Visa Sponsorship as a Machine Learning Intern
Frame your degree for specialty occupation
Internship roles can fail specialty occupation review if the job description says 'degree preferred' rather than 'required.' Ask the hiring team to confirm the role formally requires a bachelor's degree in a directly related field before you proceed with E-3 visa paperwork.
Target employers with active LCA filing history
Search the DOL's Office of Foreign Labor Certification disclosure data for employers who have filed LCAs for ML or data science roles. Prior LCA filings signal the employer already understands the E-3 process and won't stall on your offer.
Use Migrate Mate's E-3 filing service for intern timelines
Internship start dates are fixed and non-negotiable, so LCA delays are costly. Migrate Mate's E-3 filing service manages the entire process from offer to consulate appointment, minimising the risk of missing your program start date.
Clarify your Australian credentials for U.S. recruiters
A three-year Australian bachelor's degree is generally accepted as equivalent to a U.S. four-year degree for E-3 purposes, but U.S. hiring managers often flag it as a concern. Address it proactively in your application with a credential evaluation if needed.
Confirm the employer can file an LCA before accepting
Some internship programs route hires through staffing agencies or third-party employers who may not sponsor visas directly. Verify that the entity extending your offer is also the entity that will file the LCA with the DOL as your sponsoring employer.
Time your consulate appointment around program cohort dates
E-3 consular processing at Sydney, Melbourne, or Perth typically runs two to four weeks, but appointment slots fill quickly in peak hiring seasons. Book your interview as soon as the LCA is certified to avoid a gap between offer acceptance and your internship start.
E-3 Visa Machine Learning Intern: Frequently Asked Questions
How do I find Machine Learning Intern jobs that offer E-3 visa sponsorship?
Migrate Mate is built specifically for this search. It filters roles by E-3 sponsorship eligibility so you're only seeing employers who understand the visa and are open to sponsoring Australian candidates. General job boards don't surface sponsorship status reliably, which wastes time on roles that won't proceed once your visa situation comes up.
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 Intern role qualify as a specialty occupation for the E-3?
It can, but the job description has to specify that a bachelor's degree in computer science, data science, machine learning, or a directly related field is a formal requirement, not just preferred. If the posting says 'degree preferred' or lists unrelated fields as acceptable, the role may not meet USCIS's specialty occupation standard and the LCA could face complications.
How does the E-3 compare to the H-1B for Machine Learning Intern positions?
The H-1B visa has an annual cap of 85,000 slots and uses a random lottery, which means a qualifying intern can be rejected through no fault of their own. The E-3 has no lottery, no annual cap, and the 10,500 annual allocation has never been exhausted. For Australian ML interns, the E-3 is a significantly more predictable path to securing U.S. work authorization.
Can I convert an E-3 internship into a full-time role and stay on the same visa?
Yes. The E-3 doesn't restrict you to a single role or employer, but each new position requires a fresh LCA filed with the DOL and a new visa issuance or amended status. If your employer converts your internship to a full-time offer, they'll need to file a new LCA reflecting the permanent role title, location, and prevailing wage before you start in that capacity.