STEM OPT Llm Engineer Jobs
LLM Engineer roles in machine learning and natural language processing qualify for STEM OPT's 24-month extension when your degree is in computer science, data science, or a related STEM field. Your employer must be enrolled in E-Verify to hire you on STEM OPT, and you'll train under a formal I-983 plan tied to your engineering work.
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Get Access To All JobsINTRODUCTION
We are now looking for a Deep Learning Software Engineer, LLM Performance! NVIDIA is seeking an experienced Deep Learning Engineer passionate about analyzing and improving the performance of LLM inference! NVIDIA is rapidly growing our research and development for Deep Learning Inference and is seeking excellent Software Engineers at all levels of expertise to join our team. Companies around the world are using NVIDIA GPUs to power a revolution in deep learning, enabling breakthroughs in areas like LLM, Generative AI, Recommenders and Vision that have put DL into every software solution. Join the team that builds the software to enable the performance optimization, deployment and serving of these DL solutions. We specialize in developing GPU-accelerated Deep learning software like TensorRT, DL benchmarking software and performant solutions to deploy and serve these models.
Collaborate with the deep learning community to implement the latest algorithms for public release in TensorRT LLM, VLLM, SGLang and LLM benchmarks. Identify performance opportunities and optimize SoTA LLM models across the spectrum of NVIDIA accelerators, from datacenter GPUs to edge SoCs. Implement LLM inference, serving and deployment algorithms and optimizations using TensorRT LLM, VLLM, SGLang, Triton and CUDA kernels. Work and collaborate with a diverse set of teams involving performance modeling, performance analysis, kernel development and inference software development.
What you'll be doing:
- Performance optimization, analysis, and tuning of LLM, VLM and GenAI models for DL inference, serving and deployment in NVIDIA/OSS LLM frameworks.
- Scale performance of LLM models across different architectures and types of NVIDIA accelerators.
- Scale performance for max throughput, minimum latency and throughput under latency constraints.
- Contribute features and code to NVIDIA/OSS LLM frameworks, inference benchmarking frameworks, TensorRT, and Triton.
- Work with cross-collaborative teams across generative AI, automotive, image understanding, and speech understanding to develop innovative solutions.
What we need to see:
- Bachelors, Masters, PhD, or equivalent experience in relevant fields (Computer Engineering, Computer Science, EECS, AI).
- 2+ years of relevant software development experience.
- Excellent Python/C/C++ programming, software design and software engineering skills.
- Experience with a DL framework like PyTorch, JAX, TensorFlow.
Ways to stand out from the crowd:
- Prior experience with a LLM framework or a DL compiler in inference, deployment, algorithms, or implementation.
- Prior experience with performance modeling, profiling, debug, and code optimization of a DL/HPC/high-performance application.
- Architectural knowledge of CPU and GPU.
- GPU programming experience (CUDA or OpenCL).
GPU deep learning has provided the foundation for machines to learn, perceive, reason and solve problems posed using human language. The GPU started out as the engine for simulating human imagination, conjuring up the amazing virtual worlds of video games and Hollywood films. Now, NVIDIA's GPU runs deep learning algorithms, simulating human intelligence, and acts as the brain of computers, robots and self-driving cars that can perceive and understand the world. Just as human imagination and intelligence are linked, computer graphics and artificial intelligence come together in our architecture. Two modes of the human brain, two modes of the GPU. This may explain why NVIDIA GPUs are used broadly for deep learning, and NVIDIA is increasingly known as “the AI computing company.” Come, join our DL Architecture team, where you can help build the real-time, cost-effective computing platform driving our success in this exciting and quickly growing field.
LI-Hybrid
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 April 7, 2026.
This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering a diverse 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
We are now looking for a Deep Learning Software Engineer, LLM Performance! NVIDIA is seeking an experienced Deep Learning Engineer passionate about analyzing and improving the performance of LLM inference! NVIDIA is rapidly growing our research and development for Deep Learning Inference and is seeking excellent Software Engineers at all levels of expertise to join our team. Companies around the world are using NVIDIA GPUs to power a revolution in deep learning, enabling breakthroughs in areas like LLM, Generative AI, Recommenders and Vision that have put DL into every software solution. Join the team that builds the software to enable the performance optimization, deployment and serving of these DL solutions. We specialize in developing GPU-accelerated Deep learning software like TensorRT, DL benchmarking software and performant solutions to deploy and serve these models.
Collaborate with the deep learning community to implement the latest algorithms for public release in TensorRT LLM, VLLM, SGLang and LLM benchmarks. Identify performance opportunities and optimize SoTA LLM models across the spectrum of NVIDIA accelerators, from datacenter GPUs to edge SoCs. Implement LLM inference, serving and deployment algorithms and optimizations using TensorRT LLM, VLLM, SGLang, Triton and CUDA kernels. Work and collaborate with a diverse set of teams involving performance modeling, performance analysis, kernel development and inference software development.
What you'll be doing:
- Performance optimization, analysis, and tuning of LLM, VLM and GenAI models for DL inference, serving and deployment in NVIDIA/OSS LLM frameworks.
- Scale performance of LLM models across different architectures and types of NVIDIA accelerators.
- Scale performance for max throughput, minimum latency and throughput under latency constraints.
- Contribute features and code to NVIDIA/OSS LLM frameworks, inference benchmarking frameworks, TensorRT, and Triton.
- Work with cross-collaborative teams across generative AI, automotive, image understanding, and speech understanding to develop innovative solutions.
What we need to see:
- Bachelors, Masters, PhD, or equivalent experience in relevant fields (Computer Engineering, Computer Science, EECS, AI).
- 2+ years of relevant software development experience.
- Excellent Python/C/C++ programming, software design and software engineering skills.
- Experience with a DL framework like PyTorch, JAX, TensorFlow.
Ways to stand out from the crowd:
- Prior experience with a LLM framework or a DL compiler in inference, deployment, algorithms, or implementation.
- Prior experience with performance modeling, profiling, debug, and code optimization of a DL/HPC/high-performance application.
- Architectural knowledge of CPU and GPU.
- GPU programming experience (CUDA or OpenCL).
GPU deep learning has provided the foundation for machines to learn, perceive, reason and solve problems posed using human language. The GPU started out as the engine for simulating human imagination, conjuring up the amazing virtual worlds of video games and Hollywood films. Now, NVIDIA's GPU runs deep learning algorithms, simulating human intelligence, and acts as the brain of computers, robots and self-driving cars that can perceive and understand the world. Just as human imagination and intelligence are linked, computer graphics and artificial intelligence come together in our architecture. Two modes of the human brain, two modes of the GPU. This may explain why NVIDIA GPUs are used broadly for deep learning, and NVIDIA is increasingly known as “the AI computing company.” Come, join our DL Architecture team, where you can help build the real-time, cost-effective computing platform driving our success in this exciting and quickly growing field.
LI-Hybrid
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 April 7, 2026.
This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering a diverse 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 STEM OPT Authorization as a Llm Engineer
Verify your CIP code matches LLM roles
Check your degree's Classification of Instructional Programs code against STEM-designated CIP codes before applying. Computer science, electrical engineering, and data science codes typically qualify, but applied mathematics or information systems degrees vary by institution.
Confirm E-Verify enrollment before accepting offers
Ask your recruiter directly whether the company is enrolled in E-Verify before you reach the offer stage. Hiring managers at AI startups often don't know their E-Verify status, so verify through the official E-Verify employer search rather than taking their word for it.
Align your I-983 training plan to model deployment tasks
Your I-983 must connect your STEM degree to specific LLM engineering responsibilities. List concrete learning objectives tied to model fine-tuning, inference optimization, or RAG pipeline development so your DSO can certify the plan without revision requests.
Target AI labs and cloud platform teams at E-Verify employers
Use Migrate Mate to filter LLM Engineer roles by employers with confirmed E-Verify enrollment. AI research divisions at large cloud providers and well-funded AI startups that have completed Series B rounds or later are the most consistent sources of STEM OPT-compatible offers.
File your STEM OPT extension 90 days before OPT expires
USCIS requires your I-765 extension application to be received before your current EAD expires. Filing exactly 90 days early protects your work authorization gap if USCIS processing runs long, and your DSO must update SEVIS before you can submit.
Use OFLC Wage Search to benchmark your offer against prevailing wages
LLM Engineer roles map to SOC codes in computer occupations. Pull the prevailing wage for your specific job title and metro area using OFLC Wage Search before negotiating, so you can identify whether an offer reflects a legitimate rate for your work location.
Frequently Asked Questions
Does an LLM Engineer role qualify for the STEM OPT extension?
LLM Engineer positions typically qualify when your degree is in computer science, data science, electrical engineering, or another STEM-designated field. The role itself must also relate directly to your degree under the I-983 training plan your DSO certifies. Review your degree's CIP code against the official STEM-designated degree program list maintained by DHS to confirm eligibility before applying.
How do I find LLM Engineer jobs where the employer is E-Verify enrolled?
Migrate Mate filters LLM Engineer roles by employers with confirmed E-Verify enrollment, so you're not wasting applications on companies that can't legally hire STEM OPT students. You can also cross-check any employer directly through the E-Verify employer search tool. Don't rely on a recruiter's verbal confirmation since enrollment status can lapse or was never established.
What should my I-983 training plan say for an LLM Engineer position?
Your I-983 must describe how the work directly applies knowledge from your STEM degree. For LLM engineering, that means listing specific training goals such as fine-tuning transformer models, building retrieval-augmented generation pipelines, or optimizing inference latency. Generic descriptions like 'software development' are often rejected by DSOs. Tie each objective to a course, project, or competency from your degree program.
What happens to my STEM OPT authorization if my employer loses E-Verify enrollment?
If your employer loses E-Verify enrollment or withdraws, you're no longer authorized to work for them on STEM OPT. USCIS requires E-Verify participation to remain active throughout your employment, not just at the time of your extension approval. You'd need to either find a new E-Verify-enrolled employer or work with your DSO on next steps before your grace period expires.
Does cap-gap protection apply if my H-1B is selected while I'm on STEM OPT?
Yes. If your employer files an H-1B visa petition on your behalf before your STEM OPT EAD expires and you're selected in the lottery, cap-gap rules automatically extend your F-1 status and work authorization through September 30 of that fiscal year. Your STEM OPT EAD's explicit expiration date becomes irrelevant during this period. USCIS confirms cap-gap coverage based on your I-94 and the petition receipt notice.