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Full Time Llm Researcher Jobs (NOW HIRING)

DEEPREC.AI

MLE SpeechLLM Evaluations

San Francisco, CA · On-site

$250K - $350K/yr

Hybrid (3 days onsite) Full-time / Permanent DeepRec has partnered with a high-growth AI company ... The Opportunity You'll join an early Speech LLM team where your work shapes research decisions ...

Traversal

AI Researcher

New York, NY · On-site

$160K - $300K/yr

LLM & Agent Research: Prototype and evaluate prompting strategies, reasoning workflows, and tool ... S. base salary range for this full-time, in-person role in New York is $160,000-$300,000, plus ...

NIO

LLM Algorithmic Optimization Engineer

San Jose, CA · On-site

$143K - $186K/yr

Conduct research and apply cutting-edge technologies to optimize Large Language Models (LLMs) and ... The US base salary range for this full-time position is $143,200.00 - $186,000.00. * Within the ...

NIO

LLM Algorithmic Optimization Engineer

San Jose, CA

$143K - $186K/yr

Conduct research and apply cutting-edge technologies to optimize Large Language Models (LLMs) and ... The US base salary range for this full-time position is $143,200.00 - $186,000.00. * Within the ...

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All JobsFull Time Llm Researcher Jobs

Full Time Llm Researcher information

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$30K

$113.1K

$164.5K

How much do full time llm researcher jobs pay per year?

As of Jul 5, 2026, the average yearly pay for full time llm researcher in the United States is $113,102.00, according to ZipRecruiter salary data. Most workers in this role earn between $67,000.00 and $154,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Full Time LLM Researcher, and why are they important?

To thrive as a Full Time LLM Researcher, you need a deep understanding of machine learning, natural language processing, and large language model architectures, often supported by an advanced degree in computer science or a related field. Proficiency with Python, deep learning frameworks (like PyTorch or TensorFlow), and experience using large-scale compute resources are typically required. Strong analytical thinking, problem-solving abilities, and clear communication help researchers articulate findings and collaborate effectively. These skills ensure high-impact research, innovative model development, and effective integration of new technologies into real-world applications.

What does a Full Time LLM Researcher do?

A Full Time LLM Researcher studies and develops large language models (LLMs), such as those used in artificial intelligence and natural language processing applications. Their work often involves designing experiments, training models on large datasets, evaluating model performance, and publishing research findings. They may also collaborate with engineers and data scientists to implement and optimize LLMs for real-world applications. This role typically requires strong programming skills, a background in machine learning, and expertise in natural language processing.

What are some common challenges Full Time LLM Researchers face when collaborating with cross-functional teams?

Full Time LLM Researchers often collaborate with engineers, data scientists, and product managers to deploy and refine language models. A common challenge is communicating complex research findings to non-experts, ensuring alignment between research goals and product requirements. Balancing experimental innovation with practical constraints, such as computational resources and project deadlines, can also be demanding. Effective collaboration requires adaptability, strong communication skills, and the ability to translate theoretical advances into applied solutions.

What is the difference between Full Time Llm Researcher vs Part Time Llm Researcher?

AspectFull Time Llm ResearcherPart Time Llm Researcher
Work HoursTypically 35-40 hours per weekLess than 20 hours per week
Employment StatusFull-time employmentPart-time employment
CredentialsUsually requires an LLM degree, relevant research experienceSame as full-time, but may have more flexible qualifications
Work EnvironmentResearch institutions, law firms, universitiesSimilar environments, with flexible scheduling

Full Time Llm Researchers work full-time hours, often with more responsibilities and consistent schedules, while Part Time Llm Researchers have flexible hours with potentially fewer responsibilities. Both roles typically require an LLM degree and involve research in legal fields, but the full-time position offers more stability and engagement.

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What cities are hiring for Full Time Llm Researcher jobs? Cities with the most Full Time Llm Researcher job openings:
What are the most commonly searched types of Llm Researcher jobs? The most popular types of Llm Researcher jobs are:
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What job categories do people searching Full Time Llm Researcher jobs look for? The top searched job categories for Full Time Llm Researcher jobs are:
Full Time Llm Researcher jobs near you
Infographic showing various Full Time Llm Researcher job openings in the United States as of June 2026, with employment types broken down into 99% Full Time, and 1% Temporary. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution, with an average salary of $113,102 per year, or $54.4 per hour.
Research Scientist, LLM Evaluation & Post-Training

Research Scientist, LLM Evaluation & Post-Training

Centific

Remote

Full-time

Posted 13 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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