Alexander Skidanov is a seasoned software leader and co-founder of NEAR in San Francisco with 13 years building high-performance distributed systems and databases. As MemSQL’s first employee and later Director of Engineering he designed lock-free skiplists, implemented clustering and columnar storage, and led a complete parser/binder overhaul plus work on unstructured, streaming and geospatial capabilities. He pairs deep systems-level engineering with product leadership, owning both low-level concurrency primitives and large-scale production features. Beyond databases, he has made quality-focused contributions to the widely used MXNet deep-learning project, showing practical familiarity with ML tooling as well as core infrastructure.
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Role in this project:
Back-end Developer / QA Engineer
Contributions:6 commits, 3 PRs, 30 comments in 13 days
Contributions summary:Alexander primarily contributed to bug fixes and unit testing within the MXNet deep learning framework. They addressed issues related to the `ConcatOp` and `EmbeddingOp` operators, ensuring correct behavior and handling of edge cases. In addition to debugging core functionality, the user updated the test suite, adding checks for the `ConcatOp` to improve code reliability and prevent future regressions. These changes demonstrate a strong understanding of the framework's internal workings and commitment to software quality.
Contributions:13 commits, 10 pushes, 1 branch in 1 year
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.