Roy Cheng
VP AI Deployment at Nebius
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top SchoolJinglei Cheng is an engineer based in Palo Alto with seven years of experience focused on optimizing deployment and inference of large language models. Currently at Eigen AI, he brings a research-driven approach honed through postdoctoral and doctoral work at University of Pittsburgh, USC, and Purdue, bridging academic rigor with production-focused ML engineering. His background spans research scientist roles in stealth startups and deep technical training (PhD in EE from USC and PhD-level work in Computer Science at Purdue), enabling him to tackle model efficiency, systems integration, and inference latency challenges. Colleagues describe him as someone who translates complex model requirements into pragmatic deployment pipelines and measurable performance gains. Though his GitHub presence is concise, his trajectory suggests a preference for focused, high-impact contributions over broad public visibility. He combines a strong academic pedigree with hands-on experience shipping LLM inference improvements in startup environments.
7 years of coding experience
9 years of employment as a software developer
Doctor of Philosophy - PhD Electrical and Electronics Engineering, Doctor of Philosophy - PhD Electrical and Electronics Engineering at University of Southern CaliforniaDoctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Purdue UniversityBachelor of Science Microelectronics and Nanoelectronics, Bachelor of Science Microelectronics and Nanoelectronics at Tsinghua University
Github Skills (40)
simulation10
quantum-computing10
reinforcement-learning9
pytorch9
machine-learning9
inference9
neural-network9
deep-learning9
system9
cuda9
transformer8
minimax8
wan8
multimodal8
llama8
Programming languages (5)
Github contributions (5)
jinleic/Jinglei_Cheng_CV
Mar 2025 - Mar 2026
Contributions:1 push, 3 branches in 11 months
jinleic/torchquantum
Jun 2023 - Jun 2023
A PyTorch-based framework for Quantum Simulation, Quantum Machine Learning, Quantum Neural Networks, Parameterized Quantum Circuits with support for easy deployments on real quantum computers.
Contributions:7 pushes in 4 days
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