Learning Specialist Visa Sponsorship Jobs in California
Learning specialist visa sponsorship jobs in California are concentrated in the tech sector, with employers like Google, Apple, Salesforce, and Meta hiring for instructional design and corporate learning roles. Universities, K-12 districts, and edtech companies across the Bay Area, Los Angeles, and San Diego also actively recruit international talent for these positions.
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Get Access To All JobsINTRODUCTION
NVIDIA is at the forefront of the generative AI revolution. We are looking for New College Graduates to join our GPU compute architecture and performance engineering team, dedicated to pre-silicon modeling, data-supported architecture exploration, and GPU microarchitecture improvement for LLM inference, agentic AI, and other generative AI workloads. As a Deep Learning Performance Architect, you will help improve the efficiency and scalability of large language models (LLMs), agentic AI systems, and other generative AI models on NVIDIA platforms. You will gain hands-on experience with pre-silicon performance modeling, correlation, bottleneck analysis, GPU architecture studies, and system-level analysis, while collaborating with engineers across kernel, hardware, and performance teams.
This role offers an opportunity to contribute to real-world studies that help shape the performance of next-generation LLMs, agentic AI systems, and other generative AI models in data center and edge environments. You will help develop the system-level modeling and correlation solutions needed for accurate projections and for guiding performance and efficiency improvements in the agentic era.
ROLE AND RESPONSIBILITIES
What you’ll be doing:
- Build or improve pre-silicon performance and system-level models to better understand workload behavior, architecture tradeoffs, and projection accuracy
- Develop correlation methodologies to compare model projections with lower-level analyses or available reference data and improve model fidelity
- Study how GPU microarchitecture and system-level design choices affect end-to-end performance, efficiency, scalability, and projection fidelity
- Identify performance bottlenecks across model execution, kernels, data movement, and system behavior
- Explore and evaluate architecture ideas that improve performance, efficiency, or model fidelity
- Collaborate with kernel, hardware, and framework teams to better understand end-to-end behavior for LLMs, agentic AI systems, and other generative AI models
- Use profiling and benchmarking tools as needed to support model development, analysis, and correlation
- Present findings and insights through clear analysis and visualization
BASIC QUALIFICATIONS
What we need to see:
- BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related field, or equivalent experience
- Good programming skills in C++ and Python, with solid software engineering fundamentals
- Basic understanding of modern deep learning models such as Transformers or LLMs
- Interest in pre-silicon modeling, GPU architecture, correlation, system-level behavior, or systems optimization
- Exposure to computer architecture, parallel programming, performance analysis, correlation, system modeling, or related systems topics through coursework, research, internships, or projects
- Familiarity with at least one ML framework, modeling environment, profiling tool, or software stack passionate about efficiency such as PyTorch, TensorRT-LLM, Triton, Nsight, vLLM, or similar
- Ability to reason about performance tradeoffs and debug technical issues
- Strong communication skills and ability to work well with others on a team
PREFERRED QUALIFICATIONS
Ways to stand out from the crowd:
- Experience with CUDA programming, GPU performance analysis, performance modeling, correlation, system-level analysis, or profiling tools
- Hands-on work with performance-sensitive systems, computer architecture, or ML infrastructure through coursework, projects, internships, or research
- Curiosity about how systems work under the hood and a strong drive to solve hard problems
- Motivation to learn quickly, take ownership, and make steady progress on open-ended work
- Ability to work independently, explore unfamiliar technical areas, and follow through on exciting problems with personal, academic, or open-source projects related to GPU architecture, performance engineering, ML systems, agentic AI systems, or developer tools
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 124,000 USD - 195,500 USD for Level 2, and 152,000 USD - 241,500 USD for Level 3. You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until October 31, 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 the generative AI revolution. We are looking for New College Graduates to join our GPU compute architecture and performance engineering team, dedicated to pre-silicon modeling, data-supported architecture exploration, and GPU microarchitecture improvement for LLM inference, agentic AI, and other generative AI workloads. As a Deep Learning Performance Architect, you will help improve the efficiency and scalability of large language models (LLMs), agentic AI systems, and other generative AI models on NVIDIA platforms. You will gain hands-on experience with pre-silicon performance modeling, correlation, bottleneck analysis, GPU architecture studies, and system-level analysis, while collaborating with engineers across kernel, hardware, and performance teams.
This role offers an opportunity to contribute to real-world studies that help shape the performance of next-generation LLMs, agentic AI systems, and other generative AI models in data center and edge environments. You will help develop the system-level modeling and correlation solutions needed for accurate projections and for guiding performance and efficiency improvements in the agentic era.
ROLE AND RESPONSIBILITIES
What you’ll be doing:
- Build or improve pre-silicon performance and system-level models to better understand workload behavior, architecture tradeoffs, and projection accuracy
- Develop correlation methodologies to compare model projections with lower-level analyses or available reference data and improve model fidelity
- Study how GPU microarchitecture and system-level design choices affect end-to-end performance, efficiency, scalability, and projection fidelity
- Identify performance bottlenecks across model execution, kernels, data movement, and system behavior
- Explore and evaluate architecture ideas that improve performance, efficiency, or model fidelity
- Collaborate with kernel, hardware, and framework teams to better understand end-to-end behavior for LLMs, agentic AI systems, and other generative AI models
- Use profiling and benchmarking tools as needed to support model development, analysis, and correlation
- Present findings and insights through clear analysis and visualization
BASIC QUALIFICATIONS
What we need to see:
- BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related field, or equivalent experience
- Good programming skills in C++ and Python, with solid software engineering fundamentals
- Basic understanding of modern deep learning models such as Transformers or LLMs
- Interest in pre-silicon modeling, GPU architecture, correlation, system-level behavior, or systems optimization
- Exposure to computer architecture, parallel programming, performance analysis, correlation, system modeling, or related systems topics through coursework, research, internships, or projects
- Familiarity with at least one ML framework, modeling environment, profiling tool, or software stack passionate about efficiency such as PyTorch, TensorRT-LLM, Triton, Nsight, vLLM, or similar
- Ability to reason about performance tradeoffs and debug technical issues
- Strong communication skills and ability to work well with others on a team
PREFERRED QUALIFICATIONS
Ways to stand out from the crowd:
- Experience with CUDA programming, GPU performance analysis, performance modeling, correlation, system-level analysis, or profiling tools
- Hands-on work with performance-sensitive systems, computer architecture, or ML infrastructure through coursework, projects, internships, or research
- Curiosity about how systems work under the hood and a strong drive to solve hard problems
- Motivation to learn quickly, take ownership, and make steady progress on open-ended work
- Ability to work independently, explore unfamiliar technical areas, and follow through on exciting problems with personal, academic, or open-source projects related to GPU architecture, performance engineering, ML systems, agentic AI systems, or developer tools
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 124,000 USD - 195,500 USD for Level 2, and 152,000 USD - 241,500 USD for Level 3. You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until October 31, 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.
Learning Specialist Job Roles in California
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Search Learning Specialist Jobs in CaliforniaLearning Specialist Jobs in California: Frequently Asked Questions
Which companies sponsor visas for learning specialists in California?
Large tech employers are among the most active sponsors for learning specialist roles in California. Companies like Google, Apple, Meta, Salesforce, and Cisco regularly file H-1B visa petitions for instructional designers and corporate learning professionals. Beyond tech, the University of California system, major hospital networks, and edtech firms such as Coursera and Chegg have also sponsored international candidates for these positions.
Which visa types are most common for learning specialist roles in California?
The H-1B is the most common visa for learning specialists in California, provided the role requires at least a bachelor's degree in a related field such as instructional design, education technology, or organizational development. Applicants from Australia may qualify for the E-3 visa, while Canadian and Mexican nationals can explore TN visa status under the USMCA. O-1 visas are an option for specialists with demonstrated distinction in the field.
How to find learning specialist visa sponsorship jobs in California?
Migrate Mate is the most direct way to find learning specialist roles in California where employers are open to visa sponsorship. The platform filters jobs specifically for international candidates, saving time spent on roles where sponsorship is not available. Filtering by California and the learning specialist category surfaces positions at tech companies, universities, and edtech employers across the Bay Area, Los Angeles, and beyond.
Which cities in California have the most learning specialist sponsorship jobs?
The San Francisco Bay Area, including San Jose and Mountain View, sees the highest concentration of sponsored learning specialist roles due to the density of large tech employers. Los Angeles follows, driven by entertainment companies, healthcare systems, and a growing edtech presence. San Diego also has demand, particularly from biotech and defense contractors that maintain formal learning and development programs for large workforces.
Are there any California-specific considerations for learning specialist visa sponsorship?
California employers sponsoring H-1B workers must pay prevailing wages as determined by the Department of Labor for the specific location, and California's high cost of living means prevailing wage levels in metros like San Francisco and San Jose are among the highest in the country. California also has strong labor protections that apply to sponsored workers. The state's concentration of large tech and edtech employers means more companies have established immigration support processes compared to other states.
What is the prevailing wage for sponsored learning specialist jobs in California?
U.S. employers sponsoring a visa must pay at least the prevailing wage, which is what workers in the same role, area, and experience level typically earn. The Department of Labor sets this rate to make sure companies aren't hiring foreign workers simply because they'd accept lower pay than a U.S. worker. It varies by job title, location, and experience. You can look up current prevailing wage rates for any occupation and location using the OFLC Wage Search page.