Hank Lin

Engineering Leader · Amazon

Hank Lin

Software Development Manager · Personalization

I build large-scale ML and personalization systems that serve millions — and stay hands-on across mobile, web, and systems, with deep roots in machine-learning research.

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About

Engineering leader, 10+ years

Engineering leader with 10+ years of experience building large-scale personalization, ranking, and ML systems. I lead cross-functional engineering and science teams to deliver distributed systems from concept to launch, with a focus on ownership, coaching, and high-impact execution.

My work is grounded in science — a Ph.D. in Computer Science and years spent deep in machine learning and NLP, from location-aware ranking to Alexa book Q&A and character-relationship modeling. I still go deep: a current project is a from-scratch Go AI — a conv-transformer network whose strength is distilled from KataGo self-play, paired with a cross-platform Rust engine. (Formerly Hanlung Lin.)

I build across platforms, too. I design and ship software end to end — mobile games in Godot, cross-platform apps in Flutter and SwiftUI, full-stack web on AWS serverless, and systems code in Rust and Python — and I like owning the whole path from idea to production.

  • Ph.D., Computer Science — University of California, Riverside
  • M.S., Computer Science — University of Electro-Communications, Japan
  • B.S., Computer Science — University of Electro-Communications, Japan

Selected Work

Projects

Seven Seas Kingdom

A pirate idle/strategy mobile game — command fleets, raid and conquer islands, and build a home port, with offline progression.

  • Multi-ship fleets and a multi-area empire with away-area simulation
  • Automated wave combat with tactics, loot, and crew/gear/forge systems
  • Deterministic seeded simulation, headless-testable (~1,957 tests)
  • Reducer-store architecture over a reusable game-agnostic SimEngine
Godot 4.7GDScriptiOS / Android

Go Player Profiler

Profiles a Go player's style from their game records — runs KataGo per game, classifies moves, and mines recurring board-zone and shape patterns.

  • Five-stage pipeline: fetch → ingest → derive → report → scout
  • Desktop GUI with unified search over online and local game libraries
  • PDF style reports; bilingual JA/EN interface
PythonKataGoPyInstaller

Large Go Model

A from-scratch Go AI: a conv-transformer "HybridNet" distilled from KataGo self-play data, with a cross-platform Rust playing/analysis engine.

  • Data pipeline over 51k games → 12.6M distillation positions
  • PyTorch training stack (muP/Muon, ONNX export)
  • Rust engine speaking GTP and KataGo analysis (works in Sabaki / KaTrain)
PyTorchRustONNXGTP

InsectWeb

A kid-friendly family insect blog with a social feed — photo diary posts, threaded comments and reactions, auto-moderation, and a species guide. Shipped to production.

  • Threaded comments with per-comment reactions and auto-toxicity moderation
  • Cognito auth with admin / parent / kid roles
  • Species guide with photo identification; auto image resizing to WebP
  • Live at insect.ninja
Next.js 15TypeScriptAWS CDKLambda / DynamoDB

KnowledgeIndexer

A CLI and MCP server that indexes Git repos into a Source → Feature → Function hierarchy via an LLM, exporting a single KNOWLEDGE.md for AI agents.

  • Provider-agnostic summarization (Claude or any OpenAI-compatible endpoint)
  • Diff detection to skip unchanged sources
  • Ships an MCP server exposing index/list/show/query/export as tools
TypeScriptNodeMCPAnthropic SDK

Engineering Method Skills

Seven project-agnostic engineering-method skills for AI coding agents — evidence discipline, debugging, design-before-build, and change control.

  • Each skill ships a SKILL.md plus a trigger eval set
  • Distilled from a real 2,000-commit game repository
  • Encodes how to verify, debug, design, and keep docs honest
Skill / MCP authoringAI agents

Expertise

Skills

Leadership

Engineering LeadershipTeam Building & MentorshipProduct Strategy & RoadmappingCross-Org Influence

Science

Machine Learning & Deep LearningDeep Learning ResearchRecommender & Ranking SystemsNatural Language Processing

Platforms & Languages

Mobile — Godot, Flutter, SwiftUIWeb — Next.js, AWS ServerlessSystems — RustPython · TypeScript

Career

Experience

  1. Software Development Manager

    Amazon — Personalization

    Feb 2024 – Present · Irvine, CA

    • Defined vision and strategy for interest-based and product-concept personalization powering Amazon retail experiences.
    • Lead a team of 8 engineers and 2 applied scientists delivering scalable ML services serving millions of customers globally.
    • Partner across product, science, and platform orgs to ensure reliability, scalability, and cross-domain applicability.

  2. Sr Software Development Engineer

    Amazon — Personalization

    Mar 2021 – Feb 2024 · Irvine, CA

    • Led the Cost-to-Serve personalization initiative across multiple organizations.
    • Designed location-aware recommendation ranking components to improve regional relevance.
    • Managed and developed a team of 4; coached multiple promotions.

  3. Sr Software Development Engineer

    Amazon — Kindle

    Apr 2018 – Mar 2021 · Seattle, WA

    • Led design and development of book-specific Question & Answer capabilities for Alexa.
    • Built intent classification models to interpret natural-language book queries.
    • Developed an NLP system modeling character relationships in fiction books.

  4. Software Development Engineer

    Amazon — Prime

    Jan 2015 – Apr 2018 · Seattle, WA

    • Served as engineering POC for the Amazon Fresh integration initiative.
    • Developed a simplified global Prime subscription experience for millions of customers.
    • Supported Prime launches in India, China, and EU markets.