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Remote Llm Trainer information

What is a remote LLM trainer?

Remote LLM Trainers are professionals who work from any location to help train large language models (LLMs) by providing high-quality data, evaluating model outputs, and refining model behavior. They may annotate data, review AI-generated content, or design prompts and tasks to improve the model's performance. These roles are crucial in ensuring that LLMs become more accurate, safe, and useful across various applications. Remote LLM Trainers often have backgrounds in language, linguistics, data science, or related fields and rely on digital tools to collaborate with AI development teams.

What does a remote LLM trainer do?

As a Remote LLM Trainer, your workday often involves creating, curating, and reviewing datasets, developing prompts, and evaluating large language model outputs for quality and safety. Much of your collaboration happens asynchronously through digital channels—such as project management tools, messaging platforms, and regular video meetings—with researchers, data scientists, and fellow trainers. You may also participate in feedback sessions to discuss model behavior and share insights on improving training methodologies. Adapting to rapidly evolving project requirements and maintaining clear communication are key to success in this distributed, fast-paced environment.

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

To thrive as a Remote LLM Trainer, you need a deep understanding of machine learning, natural language processing, and large language models, typically supported by a degree in computer science or related fields. Experience with Python, deep learning frameworks like TensorFlow or PyTorch, and familiarity with annotation tools or data labeling platforms is essential. Strong communication, attention to detail, and the ability to work independently are standout soft skills in this role. These skills and qualities ensure accurate model training, effective collaboration with distributed teams, and the delivery of high-quality AI solutions.

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

AspectRemote Llm TrainerData Scientist
Required CredentialsBackground in AI, NLP, or machine learning; often a degree in computer science or related fieldDegree in computer science, statistics, or related fields; often certifications in data analysis or machine learning
Work EnvironmentRemote, collaborative teams developing and fine-tuning language modelsRemote or on-site, analyzing data, building models, and deriving insights
Employer & Industry UsageTech companies, AI startups, research institutionsTech firms, finance, healthcare, consulting, and research organizations

While both roles involve working with data and machine learning, a Remote Llm Trainer specializes in training and refining language models, whereas a Data Scientist focuses on analyzing data, building predictive models, and deriving insights across various industries.

What are the most commonly searched types of Llm Trainer jobs in California?

The most popular types of Llm Trainer jobs in California are:

What are popular job titles related to Remote Llm Trainer jobs in California?

For Remote Llm Trainer jobs in California, the most frequently searched job titles are:

What job categories do people searching Remote Llm Trainer jobs in California look for?

The top searched job categories for Remote Llm Trainer jobs in California are:

What cities in California are hiring for Remote Llm Trainer jobs?

Cities in California with the most Remote Llm Trainer job openings:

Remote Llm Trainer jobs near you
Infographic showing various Remote Llm Trainer job openings in California as of August 2026, with employment types broken down into 71% Full Time, and 29% Contract. Highlights an 100% Remote job distribution.

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

DivIHN

San Diego, CA • On-site, Remote

$110K - $152K/yr

Contractor

Posted 17 days ago

Key responsibilities

  • Develop asynchronous microservices using FastAPI with SSE or WebSockets to stream real-time LLM responses to front-end UIs.

  • Design, develop, and implement vector database infrastructure to capture information from diverse engineering sources.

  • Build and optimize data extraction and structuring pipelines for multi-modal data from unstructured documents.

Job description

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.

Apply