Andraz Kavalar is a Staff Software Engineer focused on AI safety and low-level ML infrastructure and optimization at Zoox, drawing on a decade of experience building robust systems for large-scale simulation and machine learning. His background spans senior ML engineering at Dropbox and econometrics-driven data science in antitrust and competition consulting, giving him a rare mix of production ML, simulation, and rigorous quantitative modeling. Trained at institutions including UCLA (PhD work in econometrics) and Pompeu Fabra (MA in Economics), he brings strong statistical foundations to practical engineering problems. Based in San Francisco, Andraz applies optimization and low-level infra expertise to make safety-critical AI components performant and auditable. He combines an economist’s emphasis on causal thinking with hands-on systems engineering, enabling pragmatic yet scientifically grounded approaches to AI risk mitigation.
10 years of coding experience
6 years of employment as a software developer
Graduate Diploma (1st year of the two-year MPhil), Economics, Graduate Diploma (1st year of the two-year MPhil), Economics at University of Cambridge
University of California, Los Angeles
Bachelor of Laws (JD equivalent), Honors, Bachelor of Laws (JD equivalent), Honors at University of Ljubljana
Exchange Program, Finance, Exchange Program, Finance at Utrecht University
Master of Arts (MA), Economics, Honors, Master of Arts (MA), Economics, Honors at Universitat Pompeu Fabra
Contributions:20 commits, 16 pushes, 1 branch in 3 days
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Andraz Kavalar - Staff Software Engineer, AI Safety at Zoox