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Llm Trainer Jobs (NOW HIRING)

PenFed Credit Union

LLM Specialist

Mclean, VA · On-site

$16.50 - $21.75/hr

The LLM Specialist serves as PenFed's subject matter expert for Large Language Models (LLMs ... Support AI literacy efforts and enterprise training initiatives. * Serve as a trusted advisor and ...

Hippocratic AI

LLM Inference Engineer

Menlo Park, CA · On-site

Tell us about an LLM inference or training project that makes you proud! Whether you've optimized inference pipelines to achieve breakthrough performance, designed innovative training techniques, or ...

Bright Vision Technologies

LLM Prompt Specialist

$100K - $150K/yr

LLM Prompt Specialist - Remote Bright Vision Technologies is a technology consulting and software ... This commitment extends to all aspects of employment, including recruitment, hiring, training ...

Cliff Services Inc

AI/LLM Eng

Jersey City, NJ · On-site

AI/LLM Eng 12+ Months New jersey Must have experience in: Large Language Models (OpenAI/Azure ... training, fine-tuning, evaluation PyTorch/TensorFlow) Good to have: Cloud (Azure/AWS/Google Cloud ...

EnCharge AI

LLM Inference Deployment Engineer

$180K - $240K/yr

About the Role EnCharge AI is seeking an LLM Inference Deployment Engineer to optimize, deploy, and ... Deploy and optimize LLMs (GPT, LLaMA, Mistral, Falcon, etc.) post-training from libraries like ...

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How much do llm trainer jobs pay per hour?

As of Aug 9, 2026, the average hourly pay for 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 does an LLM trainer do?

LLM Trainers are responsible for designing and refining training datasets, developing prompts, evaluating model outputs, and working closely with engineers and data scientists to optimize large language models. Common challenges include maintaining data quality, mitigating model biases, and staying up-to-date with rapidly evolving AI research and best practices. You’ll often collaborate with cross-functional teams, communicate findings clearly, and adapt to new tools or methodologies. This dynamic environment offers opportunities for innovation and skill development, making it an excellent fit for those passionate about advancing AI technology.

What skills and qualifications are needed to be an LLM trainer?

To thrive as an LLM Trainer, you need a deep understanding of natural language processing (NLP), machine learning principles, and data annotation techniques, often supported by a background in computer science or related fields. Familiarity with tools like Python, PyTorch or TensorFlow, data labeling platforms, and version control systems is essential, along with knowledge of prompt engineering and model fine-tuning. Strong analytical thinking, attention to detail, and collaborative communication skills are crucial soft skills for working with cross-functional AI teams. These competencies are important for developing high-quality language models that meet user needs and industry standards.

What is an LLM trainer?

An LLM Trainer is responsible for training and fine-tuning large language models (LLMs) to improve their accuracy, efficiency, and relevance for specific applications. This role involves curating and preprocessing training data, designing training methodologies, and evaluating model performance. LLM Trainers work closely with data scientists, engineers, and researchers to optimize models for tasks such as natural language understanding, text generation, and conversational AI. They also ensure ethical AI practices by mitigating biases and refining model outputs.

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Llm Trainer jobs near you
Infographic showing various Llm Trainer job openings in the United States as of August 2026, with employment types broken down into 60% Full Time, and 40% Contract. Highlights an 60% In-person, and 40% Remote job distribution, with an average salary of $76,772 per year, or $36.9 per hour.

Principal High-Performance LLM Training Engineer

Nvidia Corporation

Santa Clara, CA • On-site

Full-time

Re-posted 14 days ago

Nvidia rating

9.6

Company rating: 9.6 out of 10

Based on 17 frontline employees who took The Breakroom Quiz

8th of 242 rated software companies

Job description

NVIDIA is seeking a Principal Engineer to drive the performance of large-scale AI training and post-training workloads across NVIDIA's full hardware and software stack. This role sits at the intersection of distributed training, GPU architecture, systems software, deep learning frameworks, and performance engineering. You will analyze and optimize frontier-scale LLM workloads running on thousands of GPUs, drive improvements across frameworks such as PyTorch, JAX, NeMo, and NeMo RL, and use insights from real workloads to help shape future NVIDIA GPU, system, and software roadmaps.
We are looking for a deeply technical leader who can operate across abstraction layers: from application-level training behavior to framework/runtime internals, CUDA libraries, communication collectives, memory systems, networking, and GPU architecture. At this level, success means both directly improving performance directly as well as setting technical direction, raising the bar for the organization, and influencing multi-functional decisions across NVIDIA.
What you will be doing:
  • Lead end-to-end performance analysis and optimization of innovative LLM pre-training and post-training workloads on the latest NVIDIA hardware and software platforms.
  • Drive workloads closer to speed-of-light performance by identifying and removing bottlenecks across compute, memory, communication, scheduling, parallelism strategy, kernel efficiency, framework overhead, and system-level scaling.
  • Develop production-quality software, tools, models, benchmarks, and analysis infrastructure that improve training performance, efficiency, and developer velocity across NVIDIA's AI software stack.
  • Build and refine performance models, workload characterizations, and simulation methodologies to guide future GPU, networking, system, and software architecture decisions.
  • Serve as a technical authority for AI training performance, partnering closely with teams across GPU architecture, systems, CUDA libraries, compilers, networking, frameworks, product management, and applied AI.
  • Translate workload insights into concrete hardware and software recommendations, and advocate for changes that improve performance and efficiency across the AI ecosystem.
  • Mentor and provide technical leadership to engineers across the organization, helping establish best practices for large-scale AI performance analysis and optimization.

What we need to see:
  • A MS, or PhD (or equivalent experience) in Computer Science, Electrical Engineering, Computer Engineering, or a related field, with 12+ years of relevant work or research experience.
  • Demonstrated principal-level technical impact in one or more of the following areas: large-scale AI training systems, GPU performance optimization, distributed systems, high-performance computing, ML frameworks, compilers/runtimes, or hardware/software co-design.
  • Deep hands-on experience analyzing and optimizing performance of large-scale deep learning workloads, especially transformer-based models, LLM pre-training, reinforcement learning, fine-tuning, or other post-training workloads.
  • Strong understanding of GPU and AI accelerator architecture from individual accelerators to datacenter-scale systems.
  • Experience with distributed training techniques such as data parallelism, tensor parallelism, pipeline parallelism, expert parallelism, sequence parallelism, activation checkpointing, mixed precision training, and communication/computation overlap.
  • A strong track record of using profiling, tracing, benchmarking, and performance modeling tools to diagnose complex bottlenecks and drive measurable improvements.
  • Excellent communication and technical leadership skills, with the ability to influence architecture and software decisions across multiple teams without relying on direct authority.

GPU computing is the most productive and pervasive platform for deep learning and AI. It begins with the most advanced GPUs and the systems and software we build on top of them. We integrate and optimize every deep learning framework. We work with the major systems companies and every major cloud service provider to make GPUs available in data centers and in the cloud. We craft computers and software to bring AI to edge devices, such as self-driving cars and autonomous robots. AI has the potential to spur a wave of social progress unmatched since the industrial revolution.
This opportunity offers you the ability to collaborate with some of the most forward-thinking and hard-working people in the world, shaping the future of AI in a creative and autonomous work environment that encourages innovation. If you're passionate about working across the full hardware & software stack-from GPU architecture to application code-to achieve optimal performance, we want to hear from you!
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 272,000 USD - 431,250 USD.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until May 2, 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

Year founded

1993

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