Andrei Alexandru
CxA at Digital.ai
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolAndrei Alexandru is a seasoned security and product professional with 8+ years focused on applied information security, certification, and product delivery in the San Francisco Bay Area. A CISSP and specialist in DCI, FIPS 140-2/201, SCAP and postal systems evaluation, he blends deep compliance expertise with hands-on engineering and customer-facing support. At Digital.ai he progressed from senior support engineer to product owner and now CxA, driving product decisions informed by operational realities. He has practical experience evaluating and hardening systems at Arxan, Penumbra, and InfoGard, and has contributed ML evaluation tooling to the high-profile openai/evals project, reflecting an appetite for LLM measurement and automation. Collected experience across security labs, product, and support gives him a rare ability to translate rigorous certification requirements into usable, auditable product features. He’s based in San Francisco and known to still be “probably currently stuck on something,” signaling a curious, hands-on engineer who digs into hard problems.
8 years of coding experience
14 years of employment as a software developer
California Polytechnic State University, San Luis Obispo
Romanian
Github Skills (11)
machine-learning10
python10
data-analysis10
data-science9
pandas9
nlp8
bash8
natural-language-processing8
github-ci4
pydantic4
githubaction-workflow4
Programming languages (10)
Github contributions (5)
openai/evals
Jul 2023 - Mar 2024
Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.
Role in this project:
ML Engineer
ML EngineerContributions:1 review, 6 PRs, 5 comments in 8 months
Contributions summary:Andrei primarily contributed to the development and evaluation of language model-based systems within the "openai/evals" framework. Their work involved creating and refining evaluations for various language understanding tasks, including "theory of mind" and "skill acquisition." They implemented data generation scripts, experiment runners, and plotting tools to analyze and visualize model performance, along with fixing Pydantic deprecation warnings. This indicates a focus on building and assessing the capabilities of language models through targeted evaluations.
inwaves/ml-training
Oct 2020 - Apr 2024
Contributions:23 pushes, 1 branch, 1 issue in 3 years 6 months
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