- AI Research Engineer
- Boston, MA, USA
- Member since January 10, 2019
Bio
Dr. Michael Chen specializes in developing cutting-edge AI models and conducting research in natural language processing and computer vision. With a PhD in Machine Learning and extensive publication record, he bridges the gap between academic research and production systems. His work focuses on making AI more efficient, interpretable, and accessible.
Portfolio
PyTorch, Transformers, PEFT, LoRA, Distributed Training, MLflow
TensorFlow, OpenCV, YOLO, Image Segmentation, Real-time Processing
Python, BERT, GPT, Hugging Face, Named Entity Recognition, Sentiment Analysis
Experience
Availability
Contract/Consulting
Preferred Environment
Research Labs, Remote
The most amazing…
…achievement was publishing breakthrough NLP research at NeurIPS 2023 with 500+ citations.
Senior AI Research Engineer OpenAI Labs
2022 – 2024- Led research on efficient fine-tuning methods for large language models, reducing training costs by 70%.
- Published 8 papers at top-tier conferences (NeurIPS, ICML, ACL) with 1000+ combined citations.
- Developed novel architecture improvements achieving SOTA results on multiple benchmarks.
- Collaborated with product teams to deploy research models serving 10M+ users.
Technologies: PyTorch, Transformers, CUDA, Distributed Training, MLflow, Weights & Biases, Python, C++
Machine Learning Research Scientist AI Research Institute
2019 – 2022- Conducted foundational research in natural language understanding and multimodal learning.
- Built efficient training pipelines reducing experiment iteration time by 50%.
- Mentored PhD students and contributed to grant proposals securing $2M in funding.
- Open-sourced research code and models adopted by 1000+ researchers globally.
Technologies: TensorFlow, PyTorch, JAX, Python, NumPy, Pandas, Scikit-learn
PhD Research Assistant MIT CSAIL
2015 – 2019- Conducted doctoral research in deep learning for natural language processing.
- Published 6 peer-reviewed papers at major AI conferences.
- Developed novel attention mechanisms later adopted in production systems.
Technologies: Python, TensorFlow, Theano, NLTK, SpaCy, Research Methodology
2015 – 2019
PhD in Computer Science (Machine Learning)
Massachusetts Institute of Technology – Cambridge, MA
Machine Learning
Deep Learning, Neural Networks, Transformers, CNNs, RNNs, GANs, Reinforcement Learning, Transfer Learning
Languages
Python, C++, Julia, R, SQL, CUDA
Frameworks
PyTorch, TensorFlow, JAX, Hugging Face Transformers, scikit-learn, XGBoost, LightGBM
NLP
BERT, GPT, T5, LLaMA, Tokenization, Named Entity Recognition, Sentiment Analysis, Machine Translation
Computer Vision
OpenCV, YOLO, ResNet, Vision Transformers, Image Segmentation, Object Detection, GANs
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