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How much do full time llm trainer jobs pay per hour?

As of Sep 4, 2026, the average hourly pay for full time llm trainer in the United States is $36.91, according to ZipRecruiter salary data. Most workers in this role earn between $19.23 and $52.88 per hour, depending on experience, location, and employer.

What is the difference between Full Time Llm Trainer vs Part Time Llm Trainer?

AspectFull Time Llm TrainerPart Time Llm Trainer
CredentialsTypically requires a law degree, specialized training in legal education, and experience in training or teachingSame as full time, but may have less extensive experience or certifications
Work EnvironmentFull-time employment, often in law schools, legal training centers, or corporate legal departmentsPart-time roles, often freelance or contractual, with flexible hours
Employer & Industry UsageCommon in academic institutions and legal training firmsUsed by law firms, legal education providers, or online training platforms

The main difference between a Full Time Llm Trainer and a Part Time Llm Trainer lies in their employment status and work hours. Full Time Llm Trainers work on a permanent basis with a consistent schedule, often within academic or corporate settings. Part Time Llm Trainers, on the other hand, have flexible schedules and may work on a contractual basis, catering to specific courses or client needs.

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Full Time Llm Trainer jobs near you
Infographic showing various Full Time Llm Trainer job openings in the United States as of August 2026, with employment types broken down into 77% Full Time, 19% Part Time, 3% Contract, and 1% Nights. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $76,772 per year, or $36.9 per hour.

Senior Research Engineer, Foundation Model Training Infrastructure

Nvidia Corporation

Santa Clara, CA • On-site

$127K - $173K/yr

Full-time

Re-posted 3 days ago

Nvidia rating

9.6

Company rating: 9.6 out of 10

Based on 18 frontline employees who took The Breakroom Quiz

6th of 247 rated software companies

Job description

NVIDIA is searching for a senior or principal engineer who specializes in building cutting-edge infrastructure for large-scale foundation model training in the Generalist Embodied Agent Research (GEAR) group. Our team is leading Project GR00T, NVIDIA's moonshot initiative at building foundation models and full-stack technology for humanoid robots.
You will work with an amazing and collaborative research team that consistently produces influential works on multimodal foundation models, large-scale robot learning, embodied AI, and physics simulation. Our past projects include Eureka, VIMA, Voyager, MineDojo, MimicPlay, Prismer, and more. Your contributions will have a significant impact on our research projects and product roadmaps.
What you will be doing:
  • Design and maintain large-scale distributed training systems to support multi-modal foundation models for robotics.
  • Optimize GPU and cluster utilization for efficient model training and fine-tuning on massive datasets.
  • Implement scalable data loaders and preprocessors tailored for multimodal datasets, including videos, text, and sensor data.
  • Develop robust monitoring and debugging tools to ensure the reliability and performance of training workflows on large GPU clusters.
  • Collaborate with researchers to integrate cutting-edge model architectures into scalable training pipelines.

What we need to see:
  • Bachelor's degree in Computer Science, Robotics, Engineering, or a related field;
  • 10+ years of full-time industry experience in large-scale MLOps and AI infrastructure;
  • Proven experience designing and optimizing distributed training systems with frameworks like PyTorch, JAX, or TensorFlow.
  • Deep understanding of GPU acceleration, CUDA programming, and cluster management tools like Kubernetes.
  • Strong programming skills in Python and a high-performance language such as C++ for efficient system development.
  • Strong experience with large-scale GPU clusters, HPC environments, and job scheduling/orchestration tools (e.g., SLURM, Kubernetes).

Ways to stand out from the crowd:
  • Master's or PhD's degree in Computer Science, Robotics, Engineering, or a related field;
  • Demonstrated Tech Lead experience, coordinating a team of engineers and driving projects from conception to deployment;
  • Strong experience at building large-scale LLM and multimodal LLM training infrastructure;
  • Contributions to popular open-source AI frameworks or research publications in top-tier AI conferences, such as NeurIPS, ICRA, ICLR, CoRL.

NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and productive people in the world. Please join us and be part of the forefront of developing general-purpose robots and large-scale foundation models!
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD - 356,500 USD.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until January 13, 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.

What Nvidia employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

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About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

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