# Faran Ahmad > Software Engineer at Meta | IIT Delhi, CSE Location: San Francisco, California, United States Profile: https://flows.cv/faranahmad I am a Software Engineer with interest and demonstrated experience in building large scale AI/ML and Data Infrastructure / Platforms. I enjoy solving scalability, reliability and efficiency related problems providing critical contributions to the company's top line business metrics. Currently, I work within AI Infra PyTorch org within Meta on Inference Enablement for large scale generative and sparse recommender models onto heterogeneous hardwares. For 4.5+ years, I worked within the Ads ML and AI Infra - Feature & Training Data Infra Org at Meta where we built ML compiler driven Feature Engineering Platform and Infra that enables feature authoring using expressive pythonic language and does a multi-query optimizations to generate efficient streaming and batch data pipelines. It also applies privacy enforcement and serves these features (leveraging custom C++/Velox DAG engine) at a massive scale for ML inference/training within Ads ranking and delivery, Modern Recommendation Systems for FB/IG, Integrity, Commerce, Search, Shops etc. For a couple of years, I have also worked within Ads Realtime Delivery org where I contributed to building an innovative Search + SQL engine driven analytics platform optimized for providing estimates of very large search queries with strict SLAs (< 20ms). This also involved building large-scale custom stream processing and batch systems to store peta-bytes of data in online storage systems enabling us to achieve this. The platform Is used for multiple use cases within Ads targeting, Bidding, Pacing, Audience / Pre-Campaign / Delivery insights, Payments Risk etc. ## Work Experience ### Staff Software Engineer @ Meta Jan 2017 – Present | Menlo Park 1. AI Infra - PyTorch Inference Enablement for RecSys Post training model optimizations to scale sparse / sequential arch of the generative recommendation models for GPU distributed inference at massive scale (10+TB). 2. Ads ML & AI Infra - Feature and Training Data Infra TL within Realtime Feature Infra team where I worked on improving feature freshness, enabling new feature paradigms and modernizing company wide feature infra stack to deliver huge product wins across Ads, Feeds, IG and Integrity teams. - Rearchitected our realtime/streaming feature infrastructure for Ads Ranking to improve feature freshness from 10+ min -> < 10s leveraging Kappa architecture and deliver $100 million+ revenue YoY. - Enhanced the feature platform to support generation and serving of near realtime (10+ min freshness) graph learning (e.g. PPR, GNN etc) features widely used for user representation and ads ranking use cases (user features, ads related to a specific ad etc). - Modernized the recommendation ML feature platform with rich set of feature paradigms (event based features, topK, latestN etc), achieving wide adoption across multiple FB/IG products and contributing to significant product metrics wins (e.g. Reels watch time>18%, Facebook global session ~2%, IG Session > 0.11%, Feed VPV > ~2%). - Developed several key capabilities to modernize the feature infrastructure for Integrity teams at Meta, including support for new operators, feature sharing, and ensuring seamless integration with training platforms 3. Ads Realtime Data Infra - Audience Infra - Dynamic re-sharding and Elias-Fano encoding of the data to deliver 30% storage optimization for 1+ PB data, 30%+ memory improvements, 20%+ CPU utilization and reducing the overall service start time from 1+ day to a few hours. - Supported new search query patterns e.g. filtering and aggregating data based on fact tables that can be combined with search queries. ### Research Intern @ Adobe Jan 2016 – Jan 2016 | ATL Lab, Bangalore, India • Developed a predictive model for click through rate(CTR) based on intrinsic content as well as customer level details. • Conducted a literature review to identify and extract features of importance. • Applied machine learning models to estimate key parameters based on extracted features. • Performed extensive statistical analysis and filtering on the experiment data to extract relevant information. • Developed a prototype for email authoring tool capable of providing high level meaningful suggestions to the author. ## Education ### Bachelor’s Degree in Computer Science Indian Institute of Technology, Delhi ### High School in Science Air Force Bal Bharati Public School, New Delhi ### High School in Science CSKM Public School, New Delhi ### Bachelor's degree in Computer Science Indian Institute of Technology, Delhi ## Contact & Social - LinkedIn: https://linkedin.com/in/faran-ahmad-14755b73 --- Source: https://flows.cv/faranahmad JSON Resume: https://flows.cv/faranahmad/resume.json Last updated: 2026-04-12