E-3 Visa Machine Learning Engineer Jobs
Machine Learning Engineer roles qualify as E-3 visa specialty occupations, and Australian nationals can secure U.S. sponsorship without entering an H-1B lottery. The E-3 visa requires a bachelor's degree in a relevant field, certifies a prevailing wage through a DOL Labor Condition Application, and renews in two-year increments with no cap.
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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.
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Get Access To All JobsTips for Finding E-3 Visa Sponsorship as a Machine Learning Engineer
Align your degree to the role
U.S. consular officers assess whether your qualification directly supports the ML engineer job description. A degree in computer science, statistics, or a related quantitative discipline strengthens the specialty occupation case; a general IT degree with no ML coursework can draw scrutiny.
Target employers with LCA filing history
Employers who have filed Labor Condition Applications before understand the DOL certification timeline. Searching DOL's OFLC disclosure data for ML-related job titles surfaces companies already familiar with the E-3 process, cutting your time-to-offer significantly.
Search verified E-3 sponsorship roles on Migrate Mate
Migrate Mate filters Machine Learning Engineer listings specifically for E-3 visa sponsorship, so you're not cold-applying to roles where the employer hasn't confirmed willingness. Use Migrate Mate's E-3 filing service to handle your LCA and visa paperwork once you land an offer.
Frame your ML expertise for U.S. job descriptions
Australian ML engineers often list tools like PyTorch and TensorFlow but omit the specific deployment contexts U.S. hiring managers scan for, such as large-scale inference infrastructure or MLOps pipelines. Rewrite your resume around production impact, not research output.
Clarify E-3 requirements before your offer letter
Before signing, confirm that your offer letter specifies your job title, duties, and degree requirement in terms that support an LCA filing. Vague titles like 'Data Engineer' can complicate the specialty occupation determination if ML work isn't explicitly documented.
Account for LCA certification in your start date
DOL certifies most LCAs within seven business days, but your employer needs to post a public notice at the worksite for ten consecutive days before filing. Build at least three weeks between offer acceptance and your proposed start date to avoid timeline pressure.
E-3 Visa Machine Learning Engineer: Frequently Asked Questions
How do I find Machine Learning Engineer jobs with E-3 visa sponsorship?
Migrate Mate is the most direct way to search. It filters Machine Learning Engineer roles specifically by E-3 sponsorship, so every listing you see is from an employer who has indicated willingness to sponsor Australian nationals. Standard job boards don't filter by visa type, which means most applications go to employers unprepared for the LCA process.
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 Engineer role qualify as a specialty occupation for the E-3?
Yes. Machine Learning Engineer is classified under SOC code 15-2051 (Data Scientists) and 15-1252 (Software Developers), both of which require at minimum a bachelor's degree in a specific technical field. The role's reliance on advanced mathematics, statistical modeling, and software engineering satisfies the DOL and USCIS specialty occupation standard.
How does the E-3 compare to the H-1B for Machine Learning Engineers?
The E-3 is available only to Australian citizens but has no lottery and no annual cap, so you can apply any time a qualifying job offer exists. The H-1B subjects most applicants to a randomized selection process with roughly a 25 percent selection rate. For Australian ML engineers, the E-3 is a direct path that doesn't depend on lottery luck.
Can I switch Machine Learning Engineer employers while on an E-3?
Yes, but your new employer must file a fresh LCA and you'll need a new E-3 visa stamp if yours is tied to the previous employer. Unlike H-1B, there's no USCIS portability provision, so you need to time the transition carefully. Your status remains valid while you're working for the original employer and during the new application process.