Todor Markov
Member Of Technical Staff at Anthropic
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolTodor Markov is a deep learning researcher and Member of Technical Staff based in San Francisco with nine years of experience building and safety-testing frontier AI systems. He has driven production releases and infrastructure at OpenAI across applied, safety, and preparedness teams—contributing statistical analysis to an early-warning system for LLM-aided biological threats—and now continues similar work at Anthropic. His background blends rigorous academic training (M.S. in Statistics and a B.S. in Symbolic Systems from Stanford) with hands-on engineering: from multiagent reinforcement learning and distributed RL tooling to refactoring multimodal moderation pipelines. Notably, he coauthored Nature-published research on optimization algorithms during his Stanford lab work, signaling a rare mix of research depth and production impact.
9 years of coding experience
3 years of employment as a software developer
Master’s Degree Statistics, Master’s Degree Statistics at Stanford University
Bulgarian, English
Github Skills (39)
mozjpeg9
quantile9
stream9
streaming8
large-files8
code-generation8
data-storage8
bz28
vms8
gzip-stream8
multi-agent8
webhdfs8
boto8
streaming-data7
gzip7
Programming languages (1)
Github contributions (5)
todor-markov/mnist-digit-recognizer
Jan 2018 - Sep 2019
Some simple deep neural net models for classifyng the MNIST digit dataset.
Contributions:4 commits, 2 PRs, 3 pushes in 1 year 8 months
pytorchdigitdeep-learningdatasetconvolutional-neural-network
todor-markov/deep-rl-algorithms
Sep 2019 - Mar 2020
Implementations of popular deep RL algorithms. Written in Pytorch
Contributions:3 commits, 2 PRs, 2 pushes in 6 months
pytorchimplementationsdeep-learningreinforcement-learningmachine-learning
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