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Staff ML Engineer at Meta | Applied AI/ML, Search & Recommendations, Ads Bidding, RL/RLHF, LLM/RAG, Dense Retrieval, Graph Neural Networks, Agentic Engineering | 14 Patents
I am a Staff / Tech Lead Machine Learning Engineer with 9 years of AI/ML experience building large-scale production systems across Search, Recommendations, Ads, Reinforcement Learning, LLM/RAG systems, Dense Retrieval, Ranking, and Graph Neural Networks. I currently work at Meta on Threads Targeting, Bidding, and Relevance, where I drive ML initiatives across reward optimization, causal/offline evaluation, multi-objective ranking, value model simulation, Multi Armed Bandit and long-horizon optimization workflows. My work focuses on translating ambiguous product and business goals into scalable ML systems with measurable impact. Previously at Roku, I led and built ML systems across search, personalization, semantic understanding, query understanding, dense retrieval, knowledge graphs, recommendation systems, and reinforcement learning. My work powered large-scale consumer experiences including voice search, categorical search, personalized retrieval, query suggestions, recommendations, and revenue vs relevance optimizations. My technical interests span frontier AI systems, applied ML research, reinforcement learning, Agentic AI, retrieval and reasoning systems, graph neural networks, personalization and ranking, large-scale model evaluation, and production-scale ML infrastructure. I am an inventor on 14 published patents across reinforcement learning, retrieval optimization, personalized ranking, heterogeneous graph neural networks, LLM transfer learning, LLM-generated training data, and multi-query projection architectures. I also serve as a Core PC Member / Reviewer for top ML Conferences.
UC San Diego
Master’s Degree, Computer Science & Engineering
January 1, 2015 – January 1, 2017
Indian Institute of Technology, Roorkee
Bachelor of Technology (B.Tech.), Electronics & Communication Engineering
January 1, 2011 – January 1, 2015
Meta
Staff Machine Learning Engineer
April 1, 2025 – Present
United States · On-site
Roku Inc.
Tech Lead - Machine Learning Engineer
July 1, 2021 – April 1, 2025
San Jose, California, United States
Roku Inc.
Software Engineer - Machine Learning
September 1, 2017 – July 1, 2021
San Jose, California, United States
UC San Diego Health Sciences
Research Assistant
August 1, 2017 – September 1, 2017
San Diego County, California, United States
University of California, San Diego
Teaching Assitant
September 1, 2016 – December 1, 2016
San Diego
Indian Institute of Technology, Roorkee
Machine Learning : Analysis of EEG Brain Signals using Machine Learning & Signal Processing Tech.
July 1, 2014 – May 1, 2015
IIT Roorkee
Institute of Mathematical Science, Chennai, India,
Internship:Computational Neuroscience - Modeling and Simulation of Izhikevich Spiking Neural Network
June 1, 2014 – July 1, 2014
Indian Statistical Institute, Chennai , India
Research Intern - Machine Learning
June 1, 2013 – May 1, 2015
Cultural Fit Analysis
The candidate's experience is heavily concentrated in Machine Learning and Research roles, which aligns well with an innovative, data-driven culture. However, the target role is 'Backend Engineer', which, while overlapping, typically emphasizes core backend services, APIs, and infrastructure more broadly than pure ML systems. While the candidate has strong technical leadership and system design skills, the direct relevance to general backend engineering might require further validation. The breadth of projects is within the ML domain, which could be a strength for ML-focused backend roles but a potential gap for general backend roles.
Soft Skills & Operational Fit
The candidate's resume highlights leadership roles (Staff ML Engineer, Tech Lead) and experience leading cross-functional initiatives, indicating strong operational fit and soft skills related to project management, team leadership, and collaboration. The descriptions suggest a proactive approach to problem-solving and strategic thinking.