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.
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Get Access To All JobsINTRODUCTION
NVIDIA is at the forefront of innovations in Artificial Intelligence, High-Performance Computing, and Visualization. Our invention—the GPU—functions as the visual cortex of modern computing and is central to groundbreaking applications from generative AI to autonomous vehicles. We are now looking for a ML Platform Engineer to help accelerate the next era of machine learning innovation.
ROLE AND RESPONSIBILITIES
In this role, you will architect, build, and scale our high-performance ML infrastructure using modern Infrastructure-as-Code practices. Your primary focus will be on creating reliable, automated platforms that empower scientists and engineers to train and deploy the most advanced ML models on some of the world’s most powerful GPU systems. Join our top team and apply your SRE and software engineering skills to craft robust, user-friendly platforms for seamless ML development.
What You'll Be Doing:
- Design, build, and maintain our core ML platform infrastructure as code, primarily using Ansible and Terraform, ensuring reproducibility and scalability across large-scale, distributed GPU clusters.
- Apply SRE principles to diagnose, troubleshoot, and resolve complex system issues across the entire stack, ensuring high availability and performance for critical AI workloads.
- Develop robust internal automation and tooling for ML workflow orchestration, resource scheduling, and platform operations, with a strong focus on software engineering best practices.
- Collaborate with ML researchers and applied scientists to understand infrastructure needs and build solutions that streamline their end-to-end experimentation.
- Evolve and operate our multi-cloud and hybrid (on-prem + cloud) environments, implementing monitoring, alerting, and incident response protocols.
- Participate in on-call rotation to provide support for platform services and infrastructure running critical ML jobs, driving root cause analysis and implementing preventative measures.
- Write high-quality, maintainable code (Python, Go) to contribute to the core orchestration platform and automate manual processes.
- Drive the adoption of modern GPU technologies and ensure smooth integration of next-generation hardware into ML pipelines (e.g., GB200, NVLink, etc.).
BASIC QUALIFICATIONS
- BS/MS in Computer Science, Engineering, or equivalent experience.
- 5+ years in software/platform engineering or SRE roles, including 3+ years focused on ML infrastructure or distributed compute systems.
- Strong proficiency in Infrastructure-as-Code (IaC) tools, specifically Ansible and Terraform, with a proven track record of building and managing production infrastructure.
- SRE-oriented mindset with extensive experience in diagnosing system-level issues, performance tuning, and ensuring platform reliability.
- Solid understanding of ML workflows and lifecycle—from data preprocessing to deployment.
- Proficiency in operating containerized workloads with Kubernetes and Docker.
- Strong software engineering skills in languages such as Python or Go, with a focus on automation, tooling, and writing production-grade code.
- Experience with Linux systems internals, networking, and performance tuning at scale.
PREFERRED QUALIFICATIONS
- Experience building or operating ML platforms supporting frameworks like PyTorch or TensorFlow at scale.
- Deep understanding of distributed training techniques (e.g., data/model parallelism, Horovod, NCCL).
- Expertise with modern CI/CD methodologies and GitOps practices.
- Passion for building developer-centric platforms with great UX and strong operational reliability.
- Proven ability to contribute code to complex orchestration or automation platforms.
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 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4. You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until June 9, 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 at the forefront of innovations in Artificial Intelligence, High-Performance Computing, and Visualization. Our invention—the GPU—functions as the visual cortex of modern computing and is central to groundbreaking applications from generative AI to autonomous vehicles. We are now looking for a ML Platform Engineer to help accelerate the next era of machine learning innovation.
ROLE AND RESPONSIBILITIES
In this role, you will architect, build, and scale our high-performance ML infrastructure using modern Infrastructure-as-Code practices. Your primary focus will be on creating reliable, automated platforms that empower scientists and engineers to train and deploy the most advanced ML models on some of the world’s most powerful GPU systems. Join our top team and apply your SRE and software engineering skills to craft robust, user-friendly platforms for seamless ML development.
What You'll Be Doing:
- Design, build, and maintain our core ML platform infrastructure as code, primarily using Ansible and Terraform, ensuring reproducibility and scalability across large-scale, distributed GPU clusters.
- Apply SRE principles to diagnose, troubleshoot, and resolve complex system issues across the entire stack, ensuring high availability and performance for critical AI workloads.
- Develop robust internal automation and tooling for ML workflow orchestration, resource scheduling, and platform operations, with a strong focus on software engineering best practices.
- Collaborate with ML researchers and applied scientists to understand infrastructure needs and build solutions that streamline their end-to-end experimentation.
- Evolve and operate our multi-cloud and hybrid (on-prem + cloud) environments, implementing monitoring, alerting, and incident response protocols.
- Participate in on-call rotation to provide support for platform services and infrastructure running critical ML jobs, driving root cause analysis and implementing preventative measures.
- Write high-quality, maintainable code (Python, Go) to contribute to the core orchestration platform and automate manual processes.
- Drive the adoption of modern GPU technologies and ensure smooth integration of next-generation hardware into ML pipelines (e.g., GB200, NVLink, etc.).
BASIC QUALIFICATIONS
- BS/MS in Computer Science, Engineering, or equivalent experience.
- 5+ years in software/platform engineering or SRE roles, including 3+ years focused on ML infrastructure or distributed compute systems.
- Strong proficiency in Infrastructure-as-Code (IaC) tools, specifically Ansible and Terraform, with a proven track record of building and managing production infrastructure.
- SRE-oriented mindset with extensive experience in diagnosing system-level issues, performance tuning, and ensuring platform reliability.
- Solid understanding of ML workflows and lifecycle—from data preprocessing to deployment.
- Proficiency in operating containerized workloads with Kubernetes and Docker.
- Strong software engineering skills in languages such as Python or Go, with a focus on automation, tooling, and writing production-grade code.
- Experience with Linux systems internals, networking, and performance tuning at scale.
PREFERRED QUALIFICATIONS
- Experience building or operating ML platforms supporting frameworks like PyTorch or TensorFlow at scale.
- Deep understanding of distributed training techniques (e.g., data/model parallelism, Horovod, NCCL).
- Expertise with modern CI/CD methodologies and GitOps practices.
- Passion for building developer-centric platforms with great UX and strong operational reliability.
- Proven ability to contribute code to complex orchestration or automation platforms.
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 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4. You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until June 9, 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.