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Senior Llm Trainer Jobs (NOW HIRING)

Nvidia

Senior Software Engineer, AI Networking

Seattle, WA

$139K - $183K/yr

The role involves working on distributed Deep Learning, particularly within LLM training and ... If you are a senior data engineer passionate about building largescale, highimpact data platforms ...

Channel Partners

Senior AI Application Engineer

Tampa, FL · On-site +1

$140K/yr

Support LLM training, fine-tuning, and private/on-prem deployment in secure, closed environments ... Senior builder instinct - moves from ambiguous problem to working architecture quickly, without ...

Channel Partners

Senior AI Application Engineer

Tampa, FL · On-site

$120K - $140K/yr

Support LLM training, fine-tuning, and private/on-prem deployment in secure, closed environments ... Senior builder instinct - moves from ambiguous problem to working architecture quickly, without ...

Channel Partners

Senior AI Application Engineer

Tampa, FL · On-site +1

$120K - $140K/yr

Support LLM training, fine-tuning, and private/on-prem deployment in secure, closed environments ... Senior builder instinct -- moves from ambiguous problem to working architecture quickly, without ...

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Senior Llm Trainer information

See salary details

$24.5K

$77.3K

$136.5K

How much do senior llm trainer jobs pay per year?

As of Jul 20, 2026, the average yearly pay for senior llm trainer in the United States is $77,350.00, according to ZipRecruiter salary data. Most workers in this role earn between $56,000.00 and $100,000.00 per year, depending on experience, location, and employer.

What is the difference between Senior Llm Trainer vs Machine Learning Engineer?

AspectSenior Llm TrainerMachine Learning Engineer
Required CredentialsAdvanced degrees in NLP, AI, or related fields; experience with language modelsDegree in CS, AI, or related; strong programming skills
Work EnvironmentResearch labs, AI companies, language model development teamsTech companies, startups, R&D departments
Employer & Industry UsagePrimarily in AI research and language model trainingAcross various industries including tech, finance, healthcare
Common Search & Comparison IntentUnderstanding roles in language model developmentTechnical implementation of AI models

The main difference is that a Senior Llm Trainer specializes in training and fine-tuning large language models, focusing on NLP tasks, while a Machine Learning Engineer develops and deploys machine learning algorithms across various applications. Both roles require strong technical skills, but their focus areas and industry applications differ.

More about Senior Llm Trainer jobs
What cities are hiring for Senior Llm Trainer jobs? Cities with the most Senior Llm Trainer job openings:
What are the most commonly searched types of Llm Trainer jobs? The most popular types of Llm Trainer jobs are:
What states have the most Senior Llm Trainer jobs? States with the most job openings for Senior Llm Trainer jobs include:
What job categories do people searching Senior Llm Trainer jobs look for? The top searched job categories for Senior Llm Trainer jobs are:
Senior Llm Trainer jobs near you
Infographic showing various Senior Llm Trainer job openings in the United States as of July 2026, with employment types broken down into 12% As Needed, 81% Full Time, 2% Part Time, 2% Contract, and 3% Nights. Highlights an 77% Physical, 4% Hybrid, and 19% Remote job distribution, with an average salary of $77,350 per year, or $37.2 per hour.

Senior Software Engineer - LLM Trainer

Kake Group

San Francisco, CA • Remote

$125K - $165K/yr

Contractor

Re-posted 25 days ago

Job description

We are looking for a Senior Software Engineer to contribute to the development and evaluation of AI training data for a leading expert human data platform for AI agents and LLMs.

In this role, you will work at the intersection of software engineering and artificial intelligence, helping AI labs and companies build better, safer, and more capable models. You will leverage your deep technical expertise to write prompts, produce reference-quality code solutions, evaluate model outputs, and provide the structured human signal that makes AI systems smarter.

This is not a traditional engineering role - it is a unique opportunity for senior engineers who want to shape how the next generation of AI understands, generates, and reasons about code.

Key Responsibilities

  • Create and review coding tasks based on real-world software engineering scenarios, including debugging, refactoring, code generation, API usage, automated tests, performance, security, and edge cases.
  • Write high-quality reference solutions that are correct, clear, testable, and aligned with task requirements.
  • Evaluate AI-generated code and responses using structured rubrics, assessing correctness, clarity, security, performance, maintainability, and instruction-following.
  • Compare multiple model responses, select the strongest answer, and justify your decision with clear technical reasoning.
  • Identify bugs, hallucinated APIs, missing edge cases, weak explanations, and poor engineering decisions in AI-generated outputs.
  • Work with terminal-based development workflows when needed, including running tests, debugging issues, managing dependencies, and navigating repositories.
  • Follow detailed guidelines consistently and participate in calibration activities to ensure high-quality, reliable evaluations.

Core Requirements

  • 5+ years of professional software engineering experience in a backend, fullstack, or systems role.
  • Strong proficiency in at least one core programming language, ideally Python, JavaScript/TypeScript, Go, Java, C++, or SQL.
  • Hands-on experience with Terminal-Bench, with the ability to evaluate AI agent performance on terminal-based tasks including compiling code, running tests, managing environments, and completing multi-step software engineering workflows.
  • Comfortable working with Git, command line/terminal, and common development workflows.
  • Ability to evaluate code critically - not only whether it works, but whether it is well-designed, secure, and maintainable.
  • Prior experience in AI data production, RLHF, data annotation, or LLM evaluation projects.
  • Excellent written and verbal communication skills in English.
  • Ability to work independently in a remote, asynchronous, fast-paced environment.
  • High attention to detail and the ability to follow complex, rubric-based guidelines consistently

Nice-to-Have

  • Experience with Python-heavy workflows, automated testing frameworks, Docker, Linux, bash, or containerized environments.
  • Experience with repo-level code reasoning, large codebases, or open-source contributions.
  • Background in backend systems, data engineering, DevOps, infrastructure, security, or large codebase.

Additional

- US Timezone Overlap: PST (GMT -8)

Please Note: Due to the high volume of applications, only shortlisted candidates will be contacted.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.