P

Pranesh Srinivasan

Software Engineer

San Francisco, California, United States15 yrs 5 mos experience
Highly StableAI ML Practitioner

Key Highlights

  • Led grounding technology for 80+ AI products at Google.
  • Improved search ranking relevance significantly.
  • Developed distributed strategies for program trading.
Stackforce AI infers this person is a skilled AI and Financial Technology Engineer.

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Skills

Core Skills

Machine LearningNatural Language ProcessingAlgorithmsFinancial Modeling

Other Skills

Artificial IntelligenceCC++Computer ScienceData AnalysisData MiningEquitiesFundamental AnalysisJavaScriptLinuxMarket ResearchProject ManagementPythonTrading Systems

About

Engineer. Systems eat outcomes. Permanent Student.

Experience

Meta

Engineer

Jul 2025Present · 8 mos

  • MSL
Machine LearningNatural Language ProcessingProject Management

Google

Engineer

Oct 2014Jun 2025 · 10 yrs 8 mos · San Francisco Bay Area

  • [2023-]: Uber TL for RAG, Tool Use on AI Overviews, Gemini & AI mode. Improving Factuality through RAG, Synthetic Data, AutoRaters & Post Training. Through several large projects, improved the Accuracy and Factuality for Google's LLM product portfolio by XX% absolute. Worked across GDM, Gemini, Google Research, Search Quality and Search Platforms to research, build and land gains.
  • [2022-]: Founded & led grounding technology (quality, research and infra) for 80+ products across Google including Gemini, Vertex, Medical LMs, Agents and several top-tier publications. SoTA quality and one of the largest NLU deployments on the planet serving half a million QPS.
  • [2021-2022]: Search as a tool for LAMDA Factuality and multi-turn settings. Built an accurate yet order of magnitude more lightweight (compared to google.com) retrieval and snippeting system for Inference & Post-training.
  • [2019-2021]: Rearchitecting the core of Search Ranking to be Transformer first significantly improving relevance by several OKR points.
  • [2017-2019]: Featured snippets quality. Improved coverage and precision of Multiple featured snippets and freshness of shown featured snippets.
  • [2014-2017]: Serving for featured snippets (WebAnswers). Low latency, high throughput NNs deployed globally. TL for Serving Infrastructure.
  • https://scholar.google.com/citations?user=RQXUtYEAAAAJ
Financial ModelingData Analysis

Goldman sachs

Program Trading Strats

Jan 2011Jan 2014 · 3 yrs

  • Stochastically controlled, distributed strategies for program trading of large baskets. Enable principal facilitation, guaranteed trades and solicitation of blind principal risk.
  • Aggregate notional traded was in $X B per day. Recruited, and helped grow team 3x.
C++JavaScript

Google

2 roles

Engineering Intern

May 2010Jul 2010 · 2 mos

  • Optimizing memory access patterns in the GMail client through new Chrome heap APIs
  • Evolved into https://www.html5rocks.com/en/tutorials/memory/effectivemanagement/
JavaScriptData Analysis

Engineering Intern

May 2009Jul 2009 · 2 mos

  • Built a client side diagnostic tool for diagnosing Google Earth and Chrome products.
  • Launched as Chrome's client diagnosis tool.

Iiit hyderabad

Research Intern

May 2008Jul 2008 · 2 mos · Hyderabad Area, India

  • Focused on Graph Theory and Cryptography.
  • Investigated polynomial algorithms for sub problems in Byzantine network fault tolerance.
  • Evaluated Cryptography <-> Graph equivalence problems.

The fourth estate

Editor

Jan 2008Jan 2009 · 1 yr · IIT Madras

  • One of five editors of The Fourth Estate, the official campus magazine of IIT Madras.
  • Led creation of the magazine's digital presence
  • Evolved into http://www.t5eiitm.org/

Education

Indian Institute of Technology, Madras

Bachelors & Masters — Computer Science and Engineering

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