Research Scientist, LLM Evaluation & Post-Training

Centific Global Solutions, Inc.; Seattle (WA); On-site; USD 120,000 - 150,000; Full time

Job summary

Centific Global Solutions, Inc. is seeking a Research Scientist for LLM Evaluation & Post-Training in Seattle, WA. This full-time role involves leading research initiatives to improve model evaluation methodologies. Responsibilities include developing evaluation frameworks and collaborating with AI industry stakeholders. Candidates should have a PhD in a relevant field, at least 5 years of experience in applied ML research, and strong proficiency in Python and ML frameworks. This position offers hybrid working options.

Qualifications

• 5+ years of relevant experience in applied ML research.
• Demonstrated experience with LLM evaluation and benchmarking.
• Strong statistical analysis skills and ability to synthesize findings.

Responsibilities

• Define and execute a rigorous research agenda focused on LLM evaluation.
• Develop and validate comprehensive evaluation frameworks.
• Engage with customer technical stakeholders to provide recommendations.

Skills

Evaluation Science & Benchmarking

LLM & Post-Training Methods

Quantitative Analysis & Scientific Rigor

Education

MS or PhD in Computer Science, Machine Learning, Statistics, Applied Mathematics

Tools

Python

PyTorch

Hugging Face

JAX/TensorFlow

Job description

Research Scientist, LLM Evaluation & Post-Training page is loaded## Research Scientist, LLM Evaluation & Post-Traininglocations: Remote Work( USA)time type: Full timeposted on: Posted 2 Days Agojob requisition id: JR107073**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(TM) 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 OverviewAs 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 CompetenciesYou 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,