Kashyap Puranik

Software Engineer

Mountain View, California, United States15 yrs experience

AI EnabledAI ML Practitioner

Key Highlights

Expertise in Trust & Safety engineering for big tech.
Led development of AI systems for high harm content.
Architected zero-trust detection services for enterprises.

Stackforce AI infers this person is a Trust & Safety engineer with expertise in AI and enterprise security solutions.

Contact

kashyap.puranik@databricks.com LinkedIn

Skills

Other Skills

AlgorithmsArtificial IntelligenceCC++Computer ScienceData MiningData StructuresDistributed SystemsInformation RetrievalJavaJavaScriptLinuxMachine LearningMapReduceMatlab

About

A veteran big tech Trust & Safety (anti-abuse/security) engineer.

Experience

Databricks

Software Engineer

Jun 2024 – Present · 1 yr 9 mos · Mountain View, California, United States · Hybrid

Trustlab

Software Engineer

Apr 2023 – May 2024 · 1 yr 1 mo · Palo Alto, California, United States · Hybrid

Engineering lead for building generative AI based Trust & Safety systems for high harm content like misinformation

Stairwell inc

Software Engineer

Feb 2022 – Apr 2023 · 1 yr 2 mos · Mountain View, California, United States · Hybrid

Lead engineer for architecting a zero-trust continuous detection and intelligence service for enterprise executables (Endpoint protection/intelligence SAAS)

Youtube

Senior Staff Software Engineer

Jan 2018 – Feb 2022 · 4 yrs 1 mo · San Bruno, California, United States

An engineering lead at the YouTube Trust & Safety team (I focus on accounts integrity, bots/botnets, fraud, scraping, search/discovery manipulation and other attacks against YouTube surfaces)

Google

2 roles

Staff Software Engineer (Tech Lead)

Jun 2011 – Dec 2018 · 7 yrs 6 mos · Mountain View, California, United States · On-site

Tech Lead for the Gmail Spam/Phishing/Malware Team. Also worked on Google apps abuse

Software Engineer Intern

May 2010 – Jul 2010 · 2 mos

Worked on a project which involved Sentiment Analysis of Google News articles. Used the method of Compositional Semantics to improve the accuracy and precision of the system.