# Yasoob Khalid Niazi > Software Engineer @ Google (YouTube) Location: San Francisco, California, United States Profile: https://flows.cv/yasoob Software Engineer at Google with previous experience in core ML infrastructure, currently focusing on building user-facing products with GenAI and LLMs. Began my career at Google on the YouTube Trust & Safety team, where I spent 3 years developing robust, low-latency ML infrastructure pipelines to identify and catch adversarial actors. Now based in NYC, I'm currently working on leveraging LLMs to build a powerful, GenAI-focused YouTube experience for users worldwide. I graduated from UC Berkeley with a B.A. in Computer Science, where my research focused on applying machine learning to computer vision (CV) tasks. Before joining Google full-time, I gained industry experience through software engineering internships at Replate and Walmart Labs. Outside of work, I'm an avid reader with a love for fantasy and classic novels. Feel free to reach out! ## Work Experience ### Software Engineer II @ Google Jan 2025 – Present | New York, New York, United States Building LLM-based applications to create a GenAI-powered user experience for all YouTube users. ### Software Engineer II @ Google Jan 2023 – Jan 2025 | San Bruno, California, United States Worked as an L4 Mid-Level SWE on ML Infra in the Account and Behavioral Abuse (ABA) team at YouTube, focused on creating and maintaining the core architecture that protects YouTube’s ecosystem from manipulation and circumvention by adversarial actors Fully owned and drove a multi-quarter effort to launch and land a notification-based pipeline for triggering ML model classification at feature readiness, reducing model enforcement latency from 20m@P50 to <1m@P50, increasing model robustness, reducing redundant model triggers, and increasing infra flexibility Introduced a new ML Infra component, leveraging UDFs in the Spanner DB, that bypassed existing SQL limitations and unlocked a set of new model feature computations (such as quantile features), while reducing feature dev velocity from ~1mo to <1wk for existing feature generation/aggregation Extended the triggering mechanism to support feature generation for graph-based ML models, unblocking launches for the first ML-based MAC detection classifier (auto-terminating MAC violative channels at >90% TVC precision) and a supervised channel-to-channel graph model (contributing ~19bps to a top-level TnS metric) Designed an automated latency monitoring system to reduce feature onboarding dev velocity by 1wk, while setting up E2E latency monitoring/alerting for 7 new channel feature families Set up E2E integration testing (using ITS) for the team’s entire low-latency ML infra pipeline, providing coverage over all systems with 4 different functional tests and connecting over 9 different services and infra components Joined the YT ABA Core oncall rotation in Q2 2024 – ensuring system reliability at <30m response time Awarded 1 spot bonus (low latency model launch) and 2 peer bonuses (improving onboarding, adding new ML signals) ### Software Engineer I @ Google Jan 2022 – Jan 2023 | San Bruno, California, United States Joined Google as an L3 New Grad SWE working on ML Infra, getting internally promoted to L4 Mid-Level SWE slightly over a year after joining Cross-collaborated with the Creator Abuse team to launch ABA’s first low-latency channel spam ML model, reducing time-to-detection for adversarial channels from 40hr@P95 to <80m@P95 and increasing model recall by 97.6% for ‘racy shorts videos’ Contributed to the launch of Plexus Real Time – the first real-time ML model (built in TensorFlow) for engagement abuse playback detection, leading to 298M/day previously unclassified, ML-eligible playbacks being marked, while also increasing model robustness to adversarial reverse engineering Worked closely with the Actor Understanding team to design and launch a low-latency solution to reduce Regretted Views from terminated channels, leading to 5M+ (previously uncaught) regretted views/day and the suspension of an additional 52K+ channels/day Designed and implemented per-engagement rule shutoff capabilities for ABA’s Despam Service, which was especially pertinent due to a recent YT Postmortem on rule overmarking ### Software Engineer Intern @ Replate Jan 2021 – Jan 2021 | Berkeley, California, United States Created new front-end pages and updating existing ones using React, given Figma design mocks and strict deadlines Introduced the React-Testing-Library (RTL) for React component testing and updated all previously created Jest tests to fit the RTL framework, drastically reducing boilerplate code and standardized the firm’s testing philosophy Configured and integrated RuboCop (Ruby linter) throughout the entire codebase, introducing Ruby linting into CI workflow Wrote extensive integration, component, and unit tests using Jest and the RTL Performed exhaustive pre-deploy quality assurance (QA) checks to smoothly introduce new features into existing products Participated in scrums, code reviews, and sprint planning in an Agile environment, while communicating directly with project managers and designers across different teams on new feature specifications and implementation details ### Software Engineer Intern @ Walmart Labs Jan 2020 – Jan 2020 | Sunnyvale, California, United States Developed a Natural Language Processing (NLP) pipeline (preprocessing, model training, analyzing, reinforcing model) using SpaCy for Walmart’s database of driver and customer reviews, resulting in the extraction of meaningful insight and key issues Created a Convolutional Neural Network (CNN) model using TensorFlow to analyze incoming reviews, distilling them into short problem statements and relevant action items, increasing readability and decreasing time spent perusing reviews Designed and implemented a custom user interface (UI) to provide a rapid-response one-stop platform for review concerns, decreasing review analysis time by 87.5% alongside providing customers with quicker and better support ### Teaching Assistant - CS 70 @ University of California, Berkeley Jan 2020 – Jan 2020 | Berkeley, California, United States Instructed a CS 70: Discrete Mathematics and Probability Theory section, having spent over 90+ teaching hours to ensure student comprehension of the curriculum by designing lesson plans, assignments, and discussion worksheets Conducted midterm review sessions for more than 100+ students, holding review lectures and creating problem sets for student practice ### Undergraduate Researcher @ University of California, Berkeley Jan 2019 – Jan 2020 | Berkeley, California Worked under Professor A. Fedyk, helping build an ML model to parse demographic characteristics from over 4M profiles (LinkedIn resumes) to better understand employment dynamics and sector-specific prejudices in the workplace Built a CNN based face detector in Tensorflow, able to detect faces 27.5% more accurate than the default HOG implementation and designed multiprocessing functionalities that reduced image preprocessing time by 30% Designed and implemented a custom CNN Classifier, 18% more accurate than pre-built models, using FairFace for transfer learning to minimize demographic bias in the model ### Teaching Assistant - CS 61B @ University of California, Berkeley Jan 2019 – Jan 2020 | Berkeley, California Instructing a CS 61B: Data Structures section of more than 40+ students, spending 75+ teaching hours to ensure student comprehension of the curriculum by working alongside course staff to design lesson plans, assignments, and discussion worksheets Helping struggling students succeed in the class by conducting 20+ hours of one-to-one advising sessions, holding weekly group office hours, and building lab mini projects ### Teaching Assistant @ Nixor College Jan 2016 – Jan 2018 | Pakistan 250+ hours of dedicated teaching for A-levels Computer Science and Mathematics. ### Chief Operations Officer - Nixor Financial Services @ Nixor College Jan 2016 – Jan 2018 | Pakistan Worked as the COO for Nixor Financial Services, spearheading Pakistan's first student-run mutual fund startup (initial capital of $5,000), generating an 80% profit in a bearish market, utilizing technical and fundamental analysis, extracting trends from graphical patterns, and building linear regression models to estimate future price fluctuations Managed a student-run credit card startup, growing the card's market share from 23% of the student population to 68%, by designing inter-firm negotiation strategies to obtain usage incentives, streamlining bill generation and capital flow processes to improve administrative efficiency, and working with DigitalPass to implement POS-terminals at local vendors to increase daily card usage Developed a dedicated financial literacy program, designing and running the entire curriculum (including lesson plans, assignments, course infrastructure, and exams), also inviting professional guest lecturers (tax lawyers, etc.) to hold seminars, resulting in 97% of the course graduates feeling they were well-prepared to enter the financial adult world ## Education ### Bachelor of Arts - BA in Computer Science University of California, Berkeley ### High School Diploma Nixor College ## Contact & Social - LinkedIn: https://linkedin.com/in/ykn --- Source: https://flows.cv/yasoob JSON Resume: https://flows.cv/yasoob/resume.json Last updated: 2026-03-31