AI/ML Research Scientist, LLM Post-Training & Evaluation

Centific•Redmond, WA

1d•$150,000 - $160,000•Hybrid

About The Position

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

Requirements

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.

Nice To Haves

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.

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.

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