Machine Learning Jobs at NVIDIA with Visa Sponsorship
Machine Learning jobs at NVIDIA involve working on some of the most demanding AI infrastructure and research problems in the industry, from GPU-accelerated model training to production inference systems. NVIDIA has a strong track record of sponsoring international talent across H-1B visa, E-3 visa, and Green Card pathways for this function.
Find Machine Learning Jobs at NVIDIAOverview
Showing 5 of 12+ Machine Learning Jobs at NVIDIA
Have you applied for this role?
Have you applied for this role?
Have you applied for this role?
Have you applied for this role?
Have you applied for this role?
See all Machine Learning Jobs at NVIDIA
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Machine Learning Jobs at NVIDIA.
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 Machine Learning Jobs at NVIDIA
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Machine Learning Jobs at NVIDIA.
Get Access To All JobsTips for Finding Machine Learning Jobs at NVIDIA
Align your portfolio to NVIDIA's ML stack
NVIDIA recruits for ML roles tied to CUDA, TensorRT, and GPU-accelerated training pipelines. Before applying, make sure your portfolio or GitHub demonstrates hands-on experience with these tools, not just general PyTorch or TensorFlow projects.
Target teams publishing active research
NVIDIA's Applied Deep Learning Research and Autonomous Vehicles groups consistently hire ML engineers and sponsor visas for those roles. Cross-referencing job postings with published papers from those teams helps you identify where active headcount actually exists.
Distinguish your E-3 eligibility early in conversations
If you're an Australian citizen, flag your E-3 eligibility during the recruiter screen. NVIDIA sponsors E-3 visas for ML roles, and because E-3 applications are processed at a consulate without a lottery, the timeline to your start date is significantly shorter than H-1B.
Prepare specialty occupation documentation before your offer
For H-1B sponsorship, USCIS evaluates whether the ML role requires a specific bachelor's degree or higher. Gather transcripts, degree equivalency evaluations, and a clear job description linking your specialization to the role before your offer letter arrives.
Use Migrate Mate to surface NVIDIA ML openings that sponsor
Search Migrate Mate to filter Machine Learning roles at NVIDIA by visa sponsorship type. You can identify which roles are actively sponsored and apply directly, rather than filtering through listings that don't confirm sponsorship upfront.
Understand the PERM timeline if you're targeting a Green Card
NVIDIA sponsors EB-2 and EB-3 Green Cards for ML staff, but the PERM labor certification process through DOL typically takes 12 to 18 months before an I-140 is filed. Factor that into how you think about long-term status planning when evaluating an offer.
Frequently Asked Questions
Does NVIDIA sponsor H-1B visas for Machine Learnings?
Yes, NVIDIA sponsors H-1B visas for Machine Learning roles. Because H-1B cap-subject petitions are subject to an annual lottery, USCIS registration typically opens in March for an October 1 start date. NVIDIA participates in this process and has a consistent history of sponsoring ML engineers and researchers through both standard and premium processing.
Which visa types does NVIDIA sponsor for Machine Learning roles?
NVIDIA sponsors H-1B visas for most international ML hires and E-3 visas for Australian citizens, which can be processed at a U.S. consulate without a lottery. For longer-term permanent residence, NVIDIA also supports EB-2 and EB-3 Green Card pathways, including PERM labor certification filed through the Department of Labor.
What qualifications does NVIDIA expect for Machine Learning positions?
NVIDIA's ML roles typically require a bachelor's, master's, or PhD in computer science, electrical engineering, or a related field, with hands-on experience in GPU computing, deep learning frameworks, and model optimization. Research-oriented roles often expect published work or demonstrated contributions to open-source ML projects. Industry experience with production-scale inference systems is valued for applied engineering positions.
How do I apply for Machine Learning jobs at NVIDIA?
You can browse and apply for sponsored Machine Learning roles at NVIDIA through Migrate Mate, which filters listings by visa sponsorship type so you can confirm eligibility before applying. From there, applications route through NVIDIA's standard hiring process, which typically includes a recruiter screen, technical assessments, and a system design or research-focused interview loop depending on the role level.
How do I time my application around the H-1B cap and NVIDIA's hiring cycle?
USCIS opens H-1B registration each March, and NVIDIA typically plans offers for international candidates to align with the October 1 cap-subject start date. If you're on OPT, confirm how much runway you have before your authorization expires. Applying in the preceding fall or winter gives NVIDIA time to move through hiring before the registration window opens.