John Doe
Research Scientist
New York, NY
Actively Searching
Multimodal generative AI researcher building reliable LLM systems for biological applications.
01
About
I am a Research Scientist and Ph.D. candidate in Computer Science at Columbia University (Ph.D. expected May 2028) with experience developing and evaluating multimodal generative AI systems at top research labs. I specialize in large-scale transformer training, robust evaluation to reduce hallucinations, and production deployment of AI systems. I'm seeking roles that apply generative and multimodal AI to biological problems, bringing rigorous evaluation and human-AI interaction expertise to scientific discovery.
02
Goals
I am seeking roles at the intersection of biology and AI, applying generative and multimodal machine learning to biological problems. I want to build reliable, interpretable AI systems that accelerate biological discovery and support domain experts.
03
Skills
Languages
Python
C++
Java
SQL
JavaScript
Frameworks
PyTorch
TensorFlow
Hugging Face
LangChain
Cloud & Tools
AWS
Docker
Kubernetes
Linux
Git
Research Areas
LLMs
NLP
Reinforcement Learning
Computer Vision
Generative AI
Multimodal Learning
Human-AI Interaction
Techniques
Retrieval-Augmented Generation (RAG)
Long-context Reasoning
Evaluation Frameworks & Model Reliability
Distributed Training Pipelines
Knowledge Retrieval
Document Understanding
AI Alignment
04
Projects
01
Evaluation Framework to Reduce Hallucinations
Developed and deployed evaluation frameworks for multimodal LLMs that measured and mitigated hallucination, improving model reliability and reducing hallucination rates by 22%. The framework supported cross-team model assessment and informed production deployment decisions.
PythonPyTorchHugging FaceLangChainKubernetes
02
Scalable Evaluation Pipeline for Generative AI
Built scalable pipelines to evaluate generative AI systems processing millions of samples daily, enabling high-throughput testing of retrieval-augmented generation and long-context reasoning approaches. Results were presented to research and engineering leadership to guide model improvements.
PythonDockerKubernetesAWS
03
Distributed Training Pipelines for Large-Scale Transformers
Designed distributed training pipelines for training large transformer models, optimizing resource usage and enabling experiments at scale. Implemented solutions using PyTorch and Kubernetes to support research on multimodal document understanding and knowledge retrieval.
PyTorchKubernetesDocker
05
Experience
Research Scientist
July 2028 – Present
OpenAI
Led research on multimodal large language models and reasoning systems, developing evaluation frameworks that improved model reliability and reduced hallucination rates by 22%. Collaborated with product, safety, and infrastructure teams to deploy production AI systems and mentored junior researchers and interns on large-scale model experimentation and AI alignment.
AI Research Intern
Summer 2026 – Summer 2026
Google DeepMind
Built scalable evaluation pipelines for generative AI systems processing millions of samples daily and conducted experiments on retrieval-augmented generation and long-context reasoning. Presented experimental findings to senior research leadership and engineering stakeholders.
Graduate Research Assistant
Aug 2023 – May 2028
Columbia AI Research Lab (Columbia University)
Conducted research on multimodal AI systems for document understanding and knowledge retrieval, published at NeurIPS and ACL, and collaborated on human-AI interaction studies. Designed distributed training pipelines for large-scale transformer models using PyTorch and Kubernetes.
06
Education
May 2028 (expected)
Ph.D. in Computer Science
Columbia University
– May 2028 (expected)
Research focus: Generative AI, Multimodal Learning, Human-AI Interaction.
May 2022
B.S. in Electrical Engineering and Computer Science
Massachusetts Institute of Technology (MIT)
– May 2022
GPA: 4.8/5.0.