ML Engineer Jobs at NVIDIA with Visa Sponsorship
ML Engineer jobs at NVIDIA sit at the intersection of GPU architecture, large-scale model training, and production inference systems. The company has a consistent track record of sponsoring work visas for engineers in this function, covering both nonimmigrant and immigrant pathways for qualified candidates.
Find ML Engineer Jobs at NVIDIAOverview
Showing 5 of 10+ ML Engineer Jobs at NVIDIA
See all ML Engineer Jobs at NVIDIA
Sign up for free to unlock all listings, filter by visa type, and get alerts for new ML Engineer Jobs at NVIDIA.
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 ML Engineer Jobs at NVIDIA
Sign up for free to unlock all listings, filter by visa type, and get alerts for new ML Engineer Jobs at NVIDIA.
Get Access To All JobsTips for Finding ML Engineer Jobs at NVIDIA
Align your portfolio with NVIDIA's research stack
NVIDIA ML Engineer roles typically require hands-on experience with CUDA, TensorRT, or large-scale distributed training. Frame your GitHub projects and publications around GPU-accelerated workloads before applying, so your credentials match the technical bar reviewers expect.
Target teams where E-3 eligibility fits
If you hold Australian citizenship, the E-3 visa pathway lets NVIDIA sponsor you without lottery risk. Identify open ML Engineer requisitions in hardware-adjacent teams like CUDA Libraries or AI Infrastructure, where Australian candidates have historically been placed.
Understand NVIDIA's internal visa timeline
NVIDIA typically initiates H-1B cap filings in March for an October 1 start. If you receive an offer after the lottery, ask your recruiter whether a cap-exempt entity or bridge arrangement is available to cover the gap period before your start date.
Prepare for speciality occupation scrutiny early
USCIS may issue an RFE if your ML Engineer title appears generalist. Before your offer letter is finalized, confirm the job description explicitly requires a degree in computer science, electrical engineering, or a directly related field, not just any technical bachelor's degree.
Use Migrate Mate to filter verified sponsoring ML roles
Browsing open roles by function and sponsorship type saves significant time. Use Migrate Mate to filter ML Engineer positions at companies with confirmed H-1B and E-3 sponsorship histories, so you apply where the pathway already exists.
Plan your Green Card timeline from day one
NVIDIA sponsors EB-2 and EB-3 PERM petitions for ML Engineers, but PERM labor certification typically takes 12 to 18 months before an I-140 is filed. Ask your recruiter when the company typically initiates PERM for your country of birth, since priority date backlogs vary significantly.
Frequently Asked Questions
Does NVIDIA sponsor H-1B visas for ML Engineers?
Yes, NVIDIA sponsors H-1B visas for ML Engineer roles. The company participates in the annual H-1B cap lottery, with registrations submitted in March for an October 1 start date. If you're already in H-1B status with another employer, NVIDIA can file an H-1B transfer so you can start before October 1 without waiting for the next cap cycle.
How do I apply for ML Engineer jobs at NVIDIA?
Apply directly through NVIDIA's careers portal, filtering by the Machine Learning or AI Engineering job family. Tailor your resume to reflect GPU computing, model optimization, or distributed training experience relevant to the specific team. You can also browse verified ML Engineer openings at NVIDIA with confirmed sponsorship eligibility through Migrate Mate before applying.
Which visa types does NVIDIA sponsor for ML Engineers?
NVIDIA sponsors H-1B visas for ML Engineers under the specialty occupation category. Australian citizens can pursue the E-3 visa, which has no lottery and allows two-year renewable status. For permanent residence, NVIDIA supports EB-2 and EB-3 Green Card pathways through the PERM labor certification process filed with the DOL.
What qualifications does NVIDIA expect for ML Engineer roles?
NVIDIA ML Engineer roles typically require a bachelor's, master's, or PhD in computer science, electrical engineering, or a closely related field, with strong emphasis on GPU programming, deep learning frameworks such as PyTorch or JAX, and production model deployment. Candidates with published research or contributions to open-source ML infrastructure tend to move faster through the technical screen process.
How do I manage my visa status while waiting for an H-1B approval at NVIDIA?
If you're transitioning from OPT or another nonimmigrant status, timing matters. NVIDIA can file your H-1B with premium processing through USCIS, which reduces the adjudication window to 15 business days. If your OPT expires before October 1, ask your immigration contact at NVIDIA whether a cap-gap extension or a bridge to another status is available to maintain continuous work authorization.