{
  "name": "Anubhav Malhotra",
  "headline": "Engineer at Meta. Builder.",
  "location": "San Francisco Bay Area",
  "summary": "Engineer at heart, with a passion for collaborating with people to bring simplified solutions to life. Currently engineering in Meta Ads: agentic platforms, ML efficiency and large-scale systems.",
  "website": "https://anubhavmalhotra.com/",
  "links": {
    "linkedin": "https://www.linkedin.com/in/anubhavmalhotra/",
    "github": "https://github.com/anubhavmalhotra",
    "scholar": "https://scholar.google.com/citations?user=cV0RaFcAAAAJ",
    "x": "https://x.com/erfahrung",
    "email": "anubhav [dot] malhotra [dot] work [at] gmail [dot] com"
  },
  "experience": [
    {
      "company": "Meta",
      "role": "Engineering lead, Meta Ads",
      "location": "Menlo Park, CA",
      "start": "2020-10",
      "end": null,
      "summary": "Engineering lead, Meta Ads. Bootstrapped first agentic solution within org. Incorporated novel techniques in training models to make them privacy friendly without impacting business performance.",
      "highlights": [
        "New focus: increase efficiency of HBM for improving GPU utilization.",
        "Bootstrapped the agentic platform behind Meta Ads' first autonomous business impact: $$M in its first four months.",
        "$$$M in incremental revenue from privacy-friendly ML in regulated ad verticals, and $$$M+ in efficiency wins in a single year.",
        "Incorporated novel techniques in training models to make them privacy friendly without impacting business performance.",
        "Founded and scaled the Ads Serving, ML Features and Data Efficiency programs, building the organization from scratch.",
        "Bootstrapped ML explainability and execution-tracing tooling from a prototype into a dedicated full-stack team.",
        "Improved startup time of core ads binaries by about 40% with zero incidents over a year-long rollout.",
        "Built IDE support for the major ads binaries, used by 1,000+ developers."
      ]
    },
    {
      "company": "Apple",
      "role": "Software engineer, Spotlight search and on-device intelligence",
      "location": "Cupertino, CA",
      "start": "2014-02",
      "end": "2020-10",
      "summary": "Spotlight search on iOS, iPadOS and macOS.",
      "highlights": [
        "Bootstrapped and led, over five-plus years, a cross-platform, privacy-first on-device ML ranking pipeline for search results.",
        "Solved latency, memory and thermal constraints to run model inference on edge hardware, including a 60% performance improvement in retrieval and ranking on older devices.",
        "Designed privacy-friendly ML features that could be used in aggregate for training.",
        "Built the on-device A/B testing setup for on-the-fly experiments, plus triage tooling for data collection and feature computation.",
        "Work featured at WWDC four consecutive years."
      ]
    },
    {
      "company": "Microsoft",
      "role": "SDE intern (R&D), Kinect for Xbox One",
      "location": "Redmond, WA",
      "start": "2013-05",
      "end": "2013-08",
      "summary": "Computer-vision ML training pipeline.",
      "highlights": [
        "Built high-resolution data capture tooling and metadata systems for the Kinect sensor training pipeline."
      ]
    },
    {
      "company": "Adobe",
      "role": "Software engineer, licensing distributed systems",
      "location": "Noida, India",
      "start": "2010-11",
      "end": "2012-08",
      "summary": "Creative Cloud licensing infrastructure.",
      "highlights": [
        "Designed and built the Creative Cloud redemption center and point-of-sale activation infrastructure, including schema design, network monitoring and end-to-end security reviews."
      ]
    }
  ],
  "education": [
    {
      "school": "Brown University",
      "degree": "M.S. Computer Science",
      "year": null
    }
  ],
  "skills": {
    "leadership": [
      "org strategy",
      "cross-matrix management",
      "roadmap planning",
      "mentorship"
    ],
    "ml_and_infrastructure": [
      "ML architecture",
      "distributed systems",
      "system efficiency (SysML)",
      "on-device and edge optimization",
      "CPU/GPU profiling",
      "privacy-preserving ML",
      "data curation strategy"
    ],
    "languages": [
      "C++",
      "Python",
      "Hack/PHP",
      "Objective-C",
      "C#"
    ]
  },
  "patents": {
    "note": "Granted US patents as listed on Google Scholar.",
    "granted": [
      {
        "title": "Systems and methods for grouping search results",
        "number": "US 12,229,167",
        "year": 2025
      },
      {
        "title": "Location-based search results",
        "number": "US 11,977,593",
        "year": 2024
      },
      {
        "title": "Scoping a system-wide search to a user-specified application",
        "number": "US 11,681,718",
        "year": 2023
      },
      {
        "title": "Systems and methods for grouping search results",
        "number": "US 11,669,550",
        "year": 2023
      },
      {
        "title": "Methods and systems for client side search ranking improvements",
        "number": "US 11,294,911",
        "year": 2022
      },
      {
        "title": "Blending learning models for search support",
        "number": "US 11,113,289",
        "year": 2021
      },
      {
        "title": "Re-ranking search results using blended learning models",
        "number": "US 11,003,672",
        "year": 2021
      },
      {
        "title": "Systems and methods for building an on-device temporal web index for user curated/preferred web content",
        "number": "US 10,621,246",
        "year": 2020
      }
    ]
  },
  "mcp": {
    "endpoint": "https://anubhavmalhotra.com/mcp",
    "transport": "streamable-http",
    "auth": "none",
    "tools": [
      "get_profile",
      "get_experience",
      "get_patents"
    ],
    "resources": [
      "anubhav://profile",
      "anubhav://readme"
    ]
  },
  "updated": "2026-09-01"
}