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Remote Llm Training Jobs (NOW HIRING)

Sigma Group

Tamil Translator (Remote) | Sigma AI

$45K - $58K/yr

... or LLM training * Strong attention to detail What will you do? Annotation - Audio/Video/Image ... remote , performed through an online platform available 24/7. This opportunity is offered for ...

Horizon3.ai

Staff Attack Engineer, AI/LLM

Get to Know Us Horizon3.ai is a fast-growing, remote cybersecurity company dedicated to the mission ... Target AI infrastructure (model serving, training pipelines, vector databases, GPU/MLOps tooling ...

Kake

Senior Software Engineer - LLM Trainer

$125K - $165K/yr

... training data. This role involves working at the intersection of software engineering and ... a remote, asynchronous, fast-paced environment • High attention to detail and the ability to ...

Channel Partners

Senior AI Application Engineer

Tampa, FL · On-site +1

$140K/yr

This role follows a hybrid, remote-flexible work model with opportunities for onsite collaboration ... Support LLM training, fine-tuning, and private/on-prem deployment in secure, closed environments ...

C the Signs

AI Data Engineer

New York, NY · Remote

$117K - $140K/yr

... requirements for LLM training and refinement Key Responsibilities * Collaborate with data ... Flexible working arrangements (remote or hybrid options available). * The opportunity to work on ...

C the Signs

AI Data Engineer

Boston, MA · Remote

$117K - $140K/yr

... requirements for LLM training and refinement Key Responsibilities * Collaborate with data ... Flexible working arrangements (remote or hybrid options available). * The opportunity to work on ...

C the Signs

AI Data Engineer

New York, NY · On-site +1

$125K - $150K/yr

... requirements for LLM training and refinement Key Responsibilities * Collaborate with data ... Flexible working arrangements (remote or hybrid options available). * The opportunity to work on ...

Channel Partners

Senior AI Application Engineer

Tampa, FL · On-site +1

$120K - $140K/yr

This role follows a hybrid, remote-flexible work model with opportunities for onsite collaboration ... Support LLM training, fine-tuning, and private/on-prem deployment in secure, closed environments ...

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Remote Llm Training information

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How much do remote llm training jobs pay per hour?

As of Jul 27, 2026, the average hourly pay for remote llm training in the United States is $42.21, according to ZipRecruiter salary data. Most workers in this role earn between $27.88 and $53.85 per hour, depending on experience, location, and employer.

What are some common challenges faced by professionals in remote LLM training roles, and how can they be addressed?

Professionals in remote LLM (Large Language Model) training roles often face challenges such as managing distributed team communication, ensuring data privacy, and handling large-scale computational resources. Staying organized with asynchronous collaboration tools and maintaining clear documentation can help streamline teamwork. Additionally, understanding cloud-based infrastructure and adhering to strict data security protocols are essential for handling sensitive datasets. Regular check-ins and knowledge-sharing sessions also foster a supportive and productive remote work environment.

What is the difference between Remote Llm Training vs Data Scientist?

AspectRemote Llm TrainingData Scientist
Required CredentialsKnowledge of NLP, machine learning, programming skillsStatistics, programming, domain expertise
Work EnvironmentRemote, collaborative teams, AI/ML companiesRemote or on-site, diverse industries
Industry UsageAI development, NLP projectsData analysis, predictive modeling

Remote Llm Training focuses on developing and fine-tuning large language models, requiring expertise in NLP and machine learning. Data Scientists analyze data to extract insights and build models across various industries. While both roles involve programming and data skills, Remote Llm Training is specialized in AI model development, whereas Data Scientists work on broader data analysis tasks.

What is remote LLM training?

Remote LLM training refers to the process of training large language models (LLMs), such as GPT or similar AI models, on distributed computing resources that are accessed remotely. This allows data scientists and AI engineers to leverage powerful hardware, like GPUs or TPUs, which may not be available locally. Remote LLM training is commonly used to handle the massive computational requirements of modern AI models and enables collaboration among teams in different locations. It also provides scalability, flexibility, and cost-effectiveness for organizations working on advanced AI projects.

What are the key skills and qualifications needed to thrive as a Remote LLM Training Specialist, and why are they important?

To excel in Remote LLM Training, you need a strong background in machine learning, natural language processing, and computer science, often demonstrated by a relevant degree or industry experience. Familiarity with frameworks like PyTorch or TensorFlow, experience with large-scale data management, and knowledge of distributed computing systems are typically required. Strong problem-solving skills, effective communication, and the ability to work independently are vital soft skills in this remote, collaborative environment. These competencies ensure efficient model training, high-quality output, and seamless teamwork across distributed teams.
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Remote Llm Training jobs near you
Infographic showing various Remote Llm Training job openings in the United States as of July 2026, with employment types broken down into 73% Full Time, 16% Part Time, and 11% Contract. Highlights an 100% Remote job distribution, with an average salary of $87,800 per year, or $42.2 per hour.

GenAI / LLM Engineer - Remote (should be able to work on PST time zones)

Rootshell Enterprise Technologies, Inc.

Remote

Other

Posted 9 days ago

Job description

GenAI/LLM Engineer
Remote (should be able to work on PST time zones)
Prefers local to bay area.
Implementing GenAI requires specialized expertise in large language models. Traditional data scientists often haven't had the opportunity to dive deep into the practical intricacies of LLMs-particularly advanced fine-tuning techniques, model compression strategies, memory optimization approaches, and specialized training workflows. This role requires a hands-on deep learning practitioner comfortable with modern frameworks and libraries specific to LLM development.
  • Enables domain-specific fine-tuning of models to Client unique utility context
  • Improves model performance while reducing computational costs through advanced optimization techniques
  • Creates Client-specific AI capabilities that address our unique operational challenges
  • Enables the CoE to move beyond generic AI tools to customized solutions that deliver higher business value

Key Responsibilities:
  • Implement and optimize advanced fine-tuning approaches (LoRA, PEFT, QLoRA) to adapt foundation models to Client domain
  • Develop systematic prompt engineering methodologies specific to utility operations, regulatory compliance, and technical documentation
  • Create reusable prompt templates and libraries to standardize interactions across multiple LLM applications and use cases
  • Implement prompt testing frameworks to quantitatively evaluate and iteratively improve prompt effectiveness
  • Establish prompt versioning systems and governance to maintain consistency and quality across applications
  • Apply model customization techniques like knowledge distillation, quantization, and pruning to reduce memory footprint and inference costs
  • Tackle memory constraints using techniques such as sharded data parallelism, GPU offloading, or CPU+GPU hybrid approaches
  • Build robust retrieval-augmented generation (RAG) pipelines with vector databases, embedding pipelines, and optimized chunking strategies
  • Design advanced prompting strategies including chain-of-thought reasoning, conversation orchestration, and agent-based approaches
  • Collaborate with the MLOps engineer to ensure models are efficiently deployed, monitored, and retrained as needed

Expected Skillset:
  • Deep Learning & NLP: Proficiency with PyTorch/TensorFlow, Hugging Face Transformers, DSPy, and advanced LLM training techniques
  • GPU/Hardware Knowledge: Experience with multi-GPU training, memory optimization, and parallelization strategies
  • LLMOps: Familiarity with workflows for maintaining LLM-based applications in production and monitoring model performance
  • Technical Adaptability: Ability to interpret research papers and implement emerging techniques (without necessarily requiring PhD-level mathematics)
  • Domain Adaptation: Skills in creating data pipelines for fine-tuning models with utility-specific content

Apply