ZipRecruiter

Log In

1

Temporary Llm Trainer Jobs (NOW HIRING)

Page Group USA

Legal Counsel - Temp

Manhattan, NY · On-site

$85 - $120/hr

The temporary Legal Counsel will provide critical legal support to the luxury skincare CPG and ... Provide training and guidance to internal stakeholders on legal best practices. MPI does not ...

Avathon

Senior AI Engineer

Pleasanton, CA

$130K - $155K/yr

Instrument and monitor LLM applications in production using observability tools, tracking cost ... Exposure to distributed training, model optimization, and scalable inference architectures

Aquent

SEO/GEO Manager- BEAUTY

Manhattan, NY · On-site

$55 - $60/hr

Temporary Salary: $55-60 Hourly Start Date: Aug 17, 2026 About Our Client Aquent is partnering with ... We also offer free online training through Aquent Gymnasium. More information on our awesome ...

TEKsystems

AI Machine Learning Engineer

Bloomington, IL · Remote

$50 - $70/hr

The LLM process joined with the prompts creates a summary of 2 pages and puts that back into the ... This person will be mentoring and training on the AI strategy in this area for all of Life ...

next page

Showing results 1-20

All Jobstmp JobsBusiness JobsMarketing JobsTemporary Llm Trainer Jobs

Temporary Llm Trainer information

See salary details

$13

$24

$36

How much do temporary llm trainer jobs pay per hour?

As of Aug 24, 2026, the average hourly pay for temporary llm trainer in the United States is $24.74, according to ZipRecruiter salary data. Most workers in this role earn between $18.75 and $26.44 per hour, depending on experience, location, and employer.

What is a temporary LLM trainer?

Temporary LLM Trainers are professionals hired on a short-term basis to assist in training large language models (LLMs) like GPT or similar AI systems. Their role typically involves curating, labeling, or generating data, evaluating model outputs, and providing feedback to improve the performance and accuracy of LLMs. These positions are often project-based and may require expertise in linguistics, data analysis, or specific subject matter. Temporary LLM Trainers help ensure the AI models are aligned with desired guidelines and ethical standards.

What are the typical responsibilities of a temporary LLM trainer?

As a Temporary LLM Trainer, your main responsibilities involve reviewing, annotating, and generating training data to help improve large language models. This often means analyzing model outputs, providing detailed feedback, and crafting example conversations or prompts. You may collaborate closely with machine learning engineers and researchers to ensure your insights directly inform model updates. While the role is project-based, it offers valuable exposure to cutting-edge AI development and can be a stepping stone to further opportunities in the field.

What are the key skills and qualifications needed to thrive as a temporary LLM trainer?

To thrive as a Temporary LLM Trainer, you need a strong background in natural language processing, prompt engineering, and familiarity with large language models, often supported by relevant academic or industry experience. Proficiency with machine learning frameworks (such as PyTorch or TensorFlow), data annotation tools, and version control systems is typically required. Attention to detail, effective communication, and adaptability are essential soft skills for collaborating with development teams and ensuring high-quality training data. These skills ensure the LLM is trained accurately and efficiently, resulting in effective and reliable AI systems.

What is the difference between Temporary Llm Trainer vs Data Annotator?

AspectTemporary Llm TrainerData Annotator
Required CredentialsRelevant degrees in AI, NLP, or related fields; technical skills in machine learningHigh school diploma or equivalent; attention to detail
Work EnvironmentTech companies, AI labs, remote or on-siteData labeling firms, tech companies, remote or on-site
Employer & Industry UsageAI development, machine learning projectsData preparation, training datasets for AI models

Temporary Llm Trainers focus on developing and fine-tuning language models, requiring technical expertise in AI and NLP. Data Annotators primarily label data to train these models, often with less technical background. Both roles are essential in AI development but differ in skills and responsibilities.

More about Temporary Llm Trainer jobs

What cities are hiring for Temporary Llm Trainer jobs?

Cities with the most Temporary 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 Temporary Llm Trainer jobs?

States with the most job openings for Temporary Llm Trainer jobs include:

What job categories do people searching Temporary Llm Trainer jobs look for?

The top searched job categories for Temporary Llm Trainer jobs are:

Temporary Llm Trainer jobs near you
Infographic showing various Temporary Llm Trainer job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 18% Part Time, 2% Contract, and 1% Nights. Highlights an 95% Physical, 1% Hybrid, and 4% Remote job distribution, with an average salary of $51,453 per year, or $24.7 per hour.

Senior Generative AI Engineer (Google Gemini & Vertex AI) - Remote

DivIHN Integration Inc

San Diego, CA • Remote

$51 - $55/hr

Contractor

Posted 6 days ago

Job description

DivIHN (pronounced “divine”) is a CMMI ML3-certified Technology and Talent solutions firm. Driven by a unique Purpose, Culture, and Value Delivery Model, we enable meaningful connections between talented professionals and forward-thinking organizations. Since our formation in 2002, organizations across commercial and public sectors have been trusting us to help build their teams with exceptional temporary and permanent talent.

Visit us at https://divihn.com/find-a-job/ to learn more and view our open positions.

 
Please apply or call one of us to learn more

For further inquiries about this opportunity, please contact one of  our Talent Specialists, Justeen at (224)-394-4903
 
Title: Senior Generative AI Engineer (Google Gemini & Vertex AI) - Remote
Duration: 6 Months with possible for extension
Location: Remote
 
Candidates must be available to work West Coast (PST) hours.
 
Only W2 candidates are eligible for this position. Third-party or C2C candidates will not be considered.
 
Day to Day Responsibilities
  • RAG & Prompt Engineering: Craft and refine effective prompts for RAG, grounding, and context tuning to achieve optimal AI performance in product development. 
  • High-Performance API Engineering: Develop asynchronous microservices (FastAPI) using Server-Sent Events (SSE) or WebSockets to stream real-time LLM responses to front-end UIs without backend timeouts. 
  • Vector Database Infrastructure: Design, develop, and implement robust Vector Databases using LLMs and modern retrieval technologies to capture information from diverse engineering sources (PDFs, design docs, regulatory guidelines). 
  • Data Extraction & Structuring Pipelines: Build and optimize pipelines to extract and structure multi-modal data (tables, text, images) from unstructured documents for LLM training, grounding, and runtime query execution. 
  • LLM Fine-Tuning & Training: Fine-tune and train generative AI models using client's engineering data and domain knowledge to create high-accuracy, domain-specific models. 
  • GenAI Application & Tool Development: Design and implement scalable backend APIs (FastAPI/REST) and UI integration interfaces so internal engineers can query knowledge bases and analyze data. 
  • Automated Requirements Generation: Develop backend functionalities to automatically generate technical requirements from design documents, user stories, and system specification files. 
  • Documentation & Knowledge Transfer: Thoroughly document architecture, code, REST endpoints, and model training procedures to enable seamless knowledge transfer to client's internal teams. 
  • Cross-Functional Collaboration: Partner closely with Subject Matter Experts (SMEs), System Engineers, and V&V Test teams to optimize AI-powered workflows. 
  • AI Guardrails, MLOps & Cost Governance: Implement hallucination checks, PII masking, and guardrails (e.g., NeMo Guardrails) for medical device context. Track token usage, latency, and costs using LangSmith or Vertex AI monitoring.
  • Collaborate closely with engineers and cross-functional teams to design and develop AI solutions.
  • Develop and integrate Generative AI applications and APIs.
  • Work with large datasets, document processing, and data pipelines.
  • Participate in problem-solving, solution design, and technical discussions.
  • Communicate effectively with team members and stakeholders.
Must-Have Qualifications :
  •  Relevant Experience & STEM Foundation: 4+ years of professional software/ML engineering experience, with a dedicated AI/ML focus in the last 1–2 years.
  •  Google Gemini / Vertex AI (Non-negotiable): Hands-on experience with the Gemini model family and Vertex AI, including deployment, grounding, and integration into production AI services.  Hands-on experience with containerization (Docker) and deploying services via Cloud Run or GKE (Kubernetes).
  • Languages & AI Libraries: Proficiency in Python and modern ML/AI frameworks (PyTorch, LangChain, LangSmith) for building autonomous LLM agents, tools, and RAG pipelines.
  • Agent Building & Tool Calling: Proven experience building AI/LLM agents and tool-calling systems in Python against unstructured, multi-source data.
  • Context Engineering & RAG: Expertise in RAG pipelines, prompt engineering, context tuning, grounding, and Vector Databases (e.g., Milvus, Postgres/Pgvector). Clear understanding of advanced RAG architecture including Hybrid Search (Vector + Keyword), Re-ranking models, and semantic caching.
  • Unstructured Data Handling (Non-negotiable): Demonstrated ability to ingest, clean, extract, and structure text, tables, and images from unstructured documents (PDFs, design docs, regulatory files) for LLM training and usage.
Role Information
  • Generative AI Engineer role.
  • Experience in API development.
  • Hands-on experience with Google Gemini and Vertex AI.
  • Knowledge of Agentic AI frameworks and architectures.
  • Strong Python development skills, including experience with LangChain and related AI frameworks.
  • Open to candidates from any industry domain.
Required Skills :
  1. Full Stack Engineering — Building applications for AI-powered services (Backend APIs + Front-End Integration).
  2.  Generative AI & LLM Platforms — 2+ years building RAG pipelines & LLM apps using Python, LangChain, and LangSmith.
  3. Agent Building & Unstructured Data — Building AI agents/tool-calling systems in Python and handling unstructured, multi-source data extraction (PDFs, docs).
  4. Strong hands-on experience in Artificial Intelligence and Generative AI
  5. Python programming expertise.
  6. Experience with LangChain and Agentic AI frameworks.
  7. Understanding data handling to pull data from PDFs, design docs, regulatory files
Preferred Skills :
  1. Direct experience architecting and serving custom REST APIs.
  2. Experience in regulated / compliance-sensitive domains (e.g., healthcare / medical device guidelines like FDA, ISO 13485).
  3. Experience integrating multiple LLM APIs beyond a single provider (OpenAI, Bedrock, Claude, or similar).
Additional Preferred Skills 
  • Direct experience designing and deploying high-throughput REST APIs (e.g., FastAPI/Flask).
  • Familiarity with medical device development regulations and compliance (e.g., FDA guidelines, ISO 13485).
  • Experience integrating multiple LLM APIs beyond a single vendor (e.g., OpenAI, AWS Bedrock, Anthropic Claude).
  • Front-end development/integration experience for UI design (e.g., Streamlit, Gradio, React/Next.js integration).
Education Requirements:
  • Minimum Bachelors in Software/Computer/IT/Systems/Biomedical Engineering + 3 years
Required Testing:
  • Technical evaluation of Python proficiency, RAG architecture concepts, and API/Agent design.
Software Skills Required:
  • Languages: Python (AsyncIO, OOP), SQL.
  • AI & Agent Frameworks: PyTorch, LangChain, LangSmith, Vertex AI SDK.
  • API & Web Frameworks: FastAPI, Flask, REST APIs, Server-Sent Events (SSE).
  • Databases & Search: Milvus, Pgvector, Qdrant, Redis (caching).
  • Front-End Integration: Streamlit, Gradio, basic React/Next.js.
  • Testing & Guardrails: pytest, JUnit, LangSmith evaluation, NeMo Guardrails.
  • DevOps & Cloud: Docker, GCP (Vertex AI, Cloud Run, GKE), Git.
Required Certifications:
  • Professional certifications specific to AI/ML (e.g., Certified AI Professional / CAIP, Google Cloud ML Engineer) considered a plus.
Interview 
  • Number of Interviews: 1,
  • Web Conference (Zoom/ TEAMs) 
  • Live problem-solving and technical exercise during the interview.

About us:
DivIHN, the 'IT Asset Performance Services' organization, provides Professional Consulting, Custom Projects, and Professional Resource Augmentation services to clients in the Mid-West and beyond. The strategic characteristics of the organization are Standardization, Specialization, and Collaboration.

DivIHN is an equal opportunity employer. DivIHN does not and shall not discriminate against any employee or qualified applicant on the basis of race, color, religion (creed), gender, gender expression, age, national origin (ancestry), disability, marital status, sexual orientation, or military status.