Research Scientist, LLM Evaluation – Post-Training

Job not on LinkedIn

🕒 June 12

🏄 California, Washington – Remote

🏄 California – Remote ☕ Washington – Remote

💵 $150k - $300k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

🧬 Research Scientist

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Thermo Fisher Scientific

WebsiteLinkedInAll Job Openings

10,000+ employees

🏥 Healthcare

💼 Consulting

📦 Logistics

Healthcare • Consulting • Logistics

Thermo Fisher Scientific is a leading global supplier of scientific instrumentation, reagents and consumables, and software services. They support the life sciences, healthcare, and analytical chemistry sectors by providing robust solutions for laboratory research and production processes. Their innovative products and services encompass a range of applications, including diagnostics, lab workflow automation, and drug discovery.

📋 Description

• 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 • Develop and validate comprehensive evaluation frameworks for LLM and multimodal systems • Lead research on frontier evaluation domains including long-context, cross-modal, and dynamic multi-turn evaluations • Analyze model behavior and failure patterns; generate actionable recommendations for model improvement • Partner with Language Data Scientists to integrate human-in-the-loop and synthetic data/evaluation strategies

🎯 Requirements

• MS or PhD in Computer Science, Machine Learning, Statistics, Applied Mathematics, AI, or a related quantitative field (PhD strongly preferred) • 5+ years of relevant experience in applied ML research or research science, with substantial work in LLMs or foundation models (graduate research counts) • Demonstrated experience with LLM evaluation, benchmarking, alignment, post-training, or model quality research • Strong foundation in experimental design, statistical analysis, and scientific reasoning for ML systems • 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)

🏖️ Benefits

• Remote work options • Professional development opportunities