Thanos Ariyanayagam

Architecting planet-scale systems at the intersection of virtualization and AI.

Currently
Software Engineer & Tech Lead, Google
Based in
Sunnyvale, CA

I build the high-performance, secure infrastructure that powers Google’s most critical workloads, including Gemini, Google DeepMind, and Waymo.

Get in touch GitHub ↗ LinkedIn ↗

Impact

Cost savings
~$30M+
ML compute fungibility & fleet virtualization
SWE-years saved
102+
Equivalent engineering capacity unlocked
Engineers led
11
Borg Isolation across a multi-million node fleet

About

I engineer the foundational infrastructure that powers the modern AI era. As a Tech Lead at Google, I specialize in designing planet-scale systems where uncompromising security meets extreme performance. My work directly enables the reliable, high-speed execution of Google’s most critical workloads, including Gemini, Google DeepMind, and Waymo.

My expertise lies at the bleeding edge of virtualization, systems programming, and high-performance computing. Whether I'm architecting next-generation C++ node runtimes that deliver over 102+ SWE-years (~$30M+ in cost savings), directing fleet-wide isolation initiatives across millions of nodes, or crafting patent-pending compiler extensions for 3D deep learning hardware, I thrive on translating highly ambiguous architectural challenges into robust, scalable realities.

I don't just write code; I build resilient engines for scale. Armed with deep expertise in modern C++, distributed systems, and low-level hardware-software co-design, my drive is to push the boundaries of what's possible with today’s hardware to unlock tomorrow's technological leaps.

Experience

Software Engineer III & Tech Lead · L4

Nov 2025 — Present

Google · Sunnyvale, CA

Leading node-level runtime architecture across AI/ML Infrastructure and fleet-wide virtualization frameworks.

  • Tech Lead — Borg Isolation Commitment

    Directing a team of 11 engineers to fortify ecosystem security by transparently converting untrusted jobs to execute inside isolated VMs across a multi-million node global fleet, rigorously balancing security with system efficiency.

  • Tech Lead — TI-VM Next-Gen Runtime

    Architecting and optimizing the future of virtualized execution environments across the fleet to maximize hardware utilization with minimal performance and memory overhead.

  • Tech Lead (Node) — AI/ML Infrastructure

    Leading node-level runtime architecture for a confidential initiative supporting Google's rapid AI growth, driving cross-functional alignment across Kernel, Networking, and Fleet Management to scale heterogeneous infrastructure.

  • NVMe-Based VM Hotplugging

    Spearheaded the architecture and end-to-end implementation of NVMe-based VM hotplugging to plug and unplug NVMe devices without passthrough systems or in-VM dependencies—delivering native host-level filesystem speed and resolving critical I/O bottlenecks for Google DeepMind’s multiprocessing training workloads.

Software Engineer II · L3

Aug 2024 — Nov 2025

Google · Sunnyvale, CA

  • ML Compute Fungibility & Fleet Virtualization Optimization

    Identified and architected next-generation C++ node runtime and virtualization optimizations enabling seamless ML compute fungibility (CPU/GPU/TPU) to maximize global fleet capacity—driving cumulative infrastructure and engineering cost savings equivalent to 102+ SWE-years (~$30M+ in cost savings) and unlocking scale for Gemini.

  • Distributed VM Resource Management Parity

    Led the architectural design and C++ implementation of a distributed resource management initiative, enabling seamless telemetry extraction and in-place VM updates to execute complex, untrusted multi-tenant ML workloads at planetary scale.

Internships

Google

Software Engineering Intern

May 2023 — Aug 2023

Engineered a high-throughput, multithread-safe C++ API in Borglet for task lifecycle management utilizing lock-free programming to minimize contention. Designed and deployed a novel hierarchical dependency-sharing architecture for modularizing fleet-wide autonomous services within the Borg cluster manager.

Intel Corporation

Software Engineer Intern

Sept 2022 — Apr 2023

Authored a patent-pending C++ compiler extension (pub. #20230237014) for a proprietary IR to support 3D deep learning graphs, unlocking full on-chip hardware acceleration. Boosted ML model inference throughput by 11% via graph-level operator fusion, memory layout transformations, and INT8 mixed-precision optimizations.

Google

Software Developer Intern

May 2022 — Aug 2022

Architected a scalable ML experimentation pipeline on GCP and deployed a highly optimized C++/Go ensembling library that boosted a production NLP API’s F1 score by 4.28% and reduced annotation errors by 50%.

Google

STEP Intern

May 2021 — Aug 2021

Enhanced C++ infrastructure connecting to SpannerDB to capture ML anomaly detection scores, resulting in an 8% increase in bad-actor suspensions and a 10% reduction in scan quota.

Selected work

Proprietary IR & Hardware Acceleration

3D Deep Learning Compiler Extension

Co-invented and authored a C++ compiler extension for a proprietary intermediate representation (IR) supporting 3D deep learning tensor graphs, unlocking full on-chip hardware acceleration and boosting inference throughput by 11%.

Multithreaded Spatial Engine & Pathfinding

OpenStreetMaps Concurrent GIS

Achieved a >56% runtime speedup in a custom C++ Geographic Information System by architecting a concurrent thread-pool engine to parallelize map parsing and pathfinding heuristics, securing a Top-6 placement for route quality.

Adversarial Multi-Agent Reinforcement Learning

NEPIADA Autonomous Drone Swarms

Designed novel multi-agent Reinforcement Learning algorithms (DQN, PPO) that outperformed state-of-the-art methods by converging to a Nash equilibrium in adversarial, partial-information environments simulating drone swarm behavior.

Zero-Allocation Game Tree Search in C

Tournament Reversi AI Engine

Developed a Top-5% ranked Reversi AI engine in C. Implemented an aggressively optimized Minimax search featuring alpha-beta pruning, Zobrist transposition tables (memoization), and killer heuristic move ordering under tight latency constraints.

Stack

Languages

  • Modern C++ (17/20/23)
  • C
  • Go
  • Python
  • SQL
  • Bash

Infrastructure & Hardware

  • Borg
  • NVMe
  • SmartNICs
  • Kubernetes
  • gRPC & Protocol Buffers
  • Spanner & BigQuery
  • GCP & AWS
  • Docker
  • Bazel & CMake

Performance & Profiling

  • Perf
  • GDB
  • Valgrind
  • Google Test (gtest)
  • PyTest
  • CI/CD Pipelines

AI / ML & Core Domains

  • Systems Design
  • Lock-Free Programming
  • Compiler Optimization
  • Distributed Systems
  • High-Performance Networking
  • PyTorch & TensorFlow
  • Model Pruning / INT8
  • RLlib

Education

University of Toronto

Bachelor of Applied Science in Computer Engineering

Sept 2019 — Apr 2024

  • High Honours (87.5%+ Cumulative Average)
  • Certificate in Artificial Intelligence
  • Minor in Engineering Business

Contact

The best way to reach me is by email.

[email protected]
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