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Research Scientist
John Doe
Research scientist specializing in multimodal generative AI, long-context reasoning, and human-AI interaction.
I am a research scientist focused on multimodal generative AI, long-context reasoning, and human-AI interaction, currently completing a Ph.D. in Computer Science at Columbia University. I have experience developing evaluation frameworks and distributed training pipelines, deploying production AI systems, and mentoring junior researchers from internships at top AI labs and industry roles.
Résumé
DownloadSkills
Languages
Python
C++
Java
SQL
JavaScript
Frameworks
PyTorch
TensorFlow
Hugging Face
LangChain
Cloud & Tools
AWS
Docker
Kubernetes
Linux
Git
Research Areas
LLMs
Natural Language Processing (NLP)
Reinforcement Learning
Computer Vision
Generative AI
Multimodal Learning
Human-AI Interaction
Retrieval-Augmented Generation (RAG)
Long-context Reasoning
Methods & Practices
Model Evaluation
Evaluation Frameworks (reliability, hallucination reduction)
Experiment Design and Large-scale Model Experimentation
AI Alignment Practices
Infrastructure
Distributed Training Pipelines
Scalable Evaluation Pipelines
Production AI System Deployment
Kubernetes-based Training & Orchestration
Professional Skills
Mentoring and Research Leadership
Cross-functional Collaboration (product, safety, infrastructure)
Research Publication and Presentation
Connect
Featured
Hallucination Reduction & Reliability Evaluation Framework
Reduced hallucination rates by 22% and improved model reliability for multimodal LLMs by designing and implementing a comprehensive evaluation framework. Integrated automated evaluation suites into production pipelines to surface failure modes and guide model iterations while coordinating with safety and infrastructure teams to operationalize metrics.
PyTorch · Hugging Face · LangChain · Kubernetes · Docker
Distributed Training Pipeline for Large-scale Transformers
Enabled large-scale training of transformer models by designing distributed training pipelines that supported research on multimodal document understanding and knowledge retrieval. Implemented scalable training using PyTorch and Kubernetes to accelerate experiment iteration for graduate and faculty research.
PyTorch · Kubernetes · Docker · AWS
Experience
Research Scientist
July 2028 – Present
OpenAI
Reduced hallucination rates by 22% and improved model reliability for multimodal large language model deployments. Achieved this by developing evaluation frameworks, collaborating with product, safety, and infrastructure teams to deploy production AI systems, and mentoring junior researchers and interns on large-scale model experimentation and AI alignment practices.
AI Research Intern
Summer 2026 – Summer 2026
Google DeepMind
Evaluation pipelines processed millions of samples daily, enabling large-scale assessment of generative AI systems. Experiments on retrieval-augmented generation and long-context reasoning informed recommendations that were presented to senior research leadership and engineering stakeholders.
Graduate Research Assistant
Aug 2023 – May 2028
Columbia AI Research Lab
Published papers at NeurIPS and ACL and advanced multimodal AI systems for document understanding and knowledge retrieval. Designed distributed training pipelines for large-scale transformer models using PyTorch and Kubernetes and collaborated with faculty and graduate researchers on human-AI interaction studies.
Education
Ph.D. in Computer Science
Aug 2023 – May 2028
Columbia University
Research Focus: Generative AI, Multimodal Learning, Human-AI Interaction
B.S. in Electrical Engineering and Computer Science
2018 – May 2022
Massachusetts Institute of Technology (MIT)
GPA: 4.8/5.0