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Llm Trainer Jobs (NOW HIRING)

Horizon3.ai

Staff Attack Engineer, AI/LLM

Target AI infrastructure (model serving, training pipelines, vector databases, GPU/MLOps tooling ... Build and extend LLM-powered applications (prompting, structured output, agentic workflows)

Turing

Client Director, Frontier Data - US

Palo Alto, CA

$255K - $325K/yr

This leader would partner with our clients (leading LLM labs) research teams to: * Identify opportunities for building training datasets to improve model capabilities and performance * Generate these ...

Turing

Client Director, Frontier Data - US

Palo Alto, CA · On-site

$255K - $325K/yr

This leader would partner with our clients (leading LLM labs) research teams to: * Identify opportunities for building training datasets to improve model capabilities and performance * Generate these ...

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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.

Research Scientist, LLM Evaluation & Post-Training

Centific

Remote

Full-time

Re-posted 19 days ago

Job description

About Centific
Centific is a frontier AI data foundry that curates diverse, high-quality data, using our purpose-built technology platforms to empower the Magnificent Seven and our enterprise clients with safe, scalable AI deployment. Our team includes more than 150 PhDs and data scientists, along with more than 4,000 AI practitioners and engineers. We harness the power of an integrated solution ecosystem-comprising industry-leading partnerships and 1.8 million vertical domain experts in more than 230 markets-to create contextual, multilingual, pre-trained datasets; fine-tuned, industry-specific LLMs; and RAG pipelines supported by vector databases. Our zero-distance innovation™ solutions for GenAI can reduce GenAI costs by up to 80% and bring solutions to market 50% faster.
Our mission is to bridge the gap between AI creators and industry leaders by bringing best practices in GenAI to unicorn innovators and enterprise customers. We aim to help these organizations unlock significant business value by deploying GenAI at scale, helping to ensure they stay at the forefront of technological advancement and maintain a competitive edge in their respective markets.
About Job
Research Scientist, LLM Evaluation & Post-Training
Company: Centific
Location: Palo Alto, CA or Seattle, WA (Hybrid/Remote)
Type: Full-time
Role Overview
As a Research Scientist, LLM Evaluation & Post-Training, you will be at the frontier of how evaluation design, measurement strategy, and feedback signals drive model improvement across Centific's AI platform products. This is a high-impact individual contributor and collaborative research role that sits at the intersection of applied ML research, enterprise AI product development, and customer-facing scientific consulting.
You will lead research programs that define next-generation evaluation-driven post-training workflows, develop rigorous benchmark frameworks, and partner directly with leading AI organizations to deliver credible, actionable model improvement insights. This role offers the opportunity to shape Centific's internal research agenda, build reusable scientific assets, and publish at top-tier venues.
Key Responsibilities
  • Research Agenda & Experimentation: Define and execute a rigorous research agenda focused on LLM evaluation and post-training, with emphasis on evaluation-driven model improvement. Design experiments to study how evaluation methodologies impact fine-tuning and post-training outcomes.
  • Evaluation Framework Development: Develop and validate comprehensive evaluation frameworks for LLM and multimodal systems, covering benchmark and task design, scoring methods, judge/model-assisted evaluation, human evaluation protocols, and robustness/stress testing.
  • Advanced Evaluation Research: Lead research on frontier evaluation domains including long-context, cross-modal, and dynamic multi-turn evaluations. Study effectiveness and limitations of existing techniques and propose improved methodologies with clear validity and scalability tradeoffs.
  • Model Behavior Analysis: Analyze model behavior and failure patterns; generate actionable recommendations for model improvement and evaluation redesign. Translate findings into practical improvements for customer solutions and Centific's internal platforms.
  • Cross-Functional Collaboration: Partner with Language Data Scientists to integrate human-in-the-loop and synthetic data/evaluation strategies, and with AI/ML Research Engineers to translate research methods into scalable evaluation and post-training pipelines.
  • Customer Engagement: Engage with customer technical stakeholders at leading AI organizations to understand evaluation goals, review methodologies, and provide expert scientific recommendations. Serve as a credible technical peer to research and engineering leaders.
  • Knowledge & IP Creation: Contribute to internal benchmark datasets, reusable evaluation frameworks, and research assets. Produce high-quality technical documentation, internal research reports, and client-facing materials explaining methods, results, assumptions, and limitations.
  • Thought Leadership: Contribute to Centific's position as a leader in LLM evaluation and post-training through publications, conference presentations, and open-source contributions.

Core Technical Competencies
You will provide technical depth and leadership across the following domains:
Evaluation Science & Benchmarking
  • Expert-level benchmark dataset and test suite design for language and multimodal models
  • Deep understanding of metric design, scoring reliability, and measurement validity
  • Experience with human evaluation methods and quality assurance (rubric design, inter-rater reliability, adjudication frameworks)

LLM & Post-Training Methods
  • Strong understanding of post-training techniques (SFT, RLHF, RLAIF, DPO, PPO, GRPO) and how training objectives interact with evaluation outcomes
  • Ability to reason about model behavior, failure modes, and performance tradeoffs across tasks and domains
  • Familiarity with alignment, safety, and robustness considerations in model evaluation

Quantitative Analysis & Scientific Rigor
  • Strong statistical analysis skills: sampling, uncertainty quantification, significance testing, error analysis, metric interpretation
  • Ability to synthesize complex experimental findings into concise, actionable recommendations for engineering and business stakeholders

Required Qualifications
  • Education: MS or PhD in Computer Science, Machine Learning, Statistics, Applied Mathematics, AI, or a related quantitative field (PhD strongly preferred).
  • Research Experience: 5+ years of relevant experience in applied ML research or research science, with substantial work in LLMs or foundation models (graduate research counts).
  • LLM Evaluation Expertise: Demonstrated experience with LLM evaluation, benchmarking, alignment, post-training, or model quality research.
  • Experimental Design: Strong foundation in experimental design, statistical analysis, and scientific reasoning for ML systems.
  • Technical Proficiency: Strong Python coding skills for research experimentation, data processing, evaluation pipelines, statistical analysis, and visualization. Hands-on experience with modern ML frameworks (PyTorch, Hugging Face, JAX/TensorFlow).
  • Evaluation Methodology: Ability to evaluate and compare human and automated evaluation methods, including tradeoffs in cost, reliability, validity, and scalability. Experience designing reproducible evaluation studies across datasets and model versions.
  • Communication: Strong written and verbal communication skills; able to present nuanced technical conclusions, assumptions, and limitations clearly to both research and non-technical audiences.

Preferred Qualifications
  • Post-Training Practice: Hands-on experience running fine-tuning or post-training experiments (SFT, preference optimization, RLHF/RLAIF-style workflows).
  • Multimodal & Long-Context: Experience with multimodal evaluation (text-image, audio, video) and long-context benchmarking in real-world settings.
  • Agentic Evaluation: Experience designing multi-turn, interactive, or agentic evaluation protocols.
  • Scientific Contribution: Publications and/or open-source benchmark contributions in LLM evaluation, post-training, alignment, or related areas at top venues (NeurIPS, ICML, ICLR, ACL, EMNLP, etc.).
  • Applied Research Consulting: Experience in customer-facing applied research, technical consulting, or cross-functional product/research collaboration.
  • Safety & Governance: Familiarity with safety, trustworthiness, and governance considerations in GenAI evaluation.

Salary: $150K - $300K Annually
Centific is an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, ancestry, citizenship status, age, mental or physical disability, medical condition, sex (including pregnancy), gender identity or expression, sexual orientation, marital status, familial status, veteran status, or any other characteristic protected by applicable law. We consider qualified applicants regardless of criminal histories, consistent with legal requirements.

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