Summary
Vadim Smolyakov is a Machine Learning Engineer II with 11 years of experience building and productionizing large-scale ML systems, now focused on serving and optimizing open-source generative models for Microsoft’s Copilot AI. His background blends deep research (PhD-level work on scalable inference and sampling at MIT) with hands-on engineering across inference stacks like Triton, vLLM, TensorRT-LLM and ONNX, and platformization using Docker and Azure. He has led end-to-end AI product efforts—from hyper-personalization and anomaly detection to LLM fine-tuning and benchmarking—while mentoring interns and driving team-facing ML communities. Notably, he pairs signal-processing roots (MIMO and communications research) with modern deep learning at scale, giving him a rare cross-domain view of model performance and system-level optimization.
11 years of coding experience
15 years of employment as a software developer
Doctor of Philosophy (PhD) Artificial Intelligence, Doctor of Philosophy (PhD) Artificial Intelligence at Massachusetts Institute of Technology
Minor Finance, Minor Finance at MIT Sloan School of Management
MASc Electrical Engineering: Wireless Architecture and Digital Communication, MASc Electrical Engineering: Wireless Architecture and Digital Communication at University of Toronto
English, Russian