Shopify Engineering

Recently @tobi shared the philosophy behind River, our Slack-native AI agent, and how it has become a teaching workshop for all of @Shopify. Below River lies the Aquifer. Principal Engineers @burkelibbey & Javier Moreno share the engineering story of how River came to be, and the substrate it runs on:

⚡ @Shopify is running sub-agents in parallel to analyze complex data over a long horizon for more accurate merchant growth forecasts at a global scale.

(@MySQL for the win) shopify.engineering/scaling-invent…

Sometimes the most unobvious-but-correct architecture decision is just... using your primary database. We tried it on one of our highest-stakes checkout paths, at BFCM scale. Here's how it went:

🔍 Come join the hunt for bugs here: shopify.com/bugbounty

📊 Bug bounty surveillance log | April '26 • 547 vulnerability reports intercepted • 232 new hackers onboarded • 42 reports awarded bounties • $180K+ distributed to hunters

Read more on our Eng Blog: shopify.engineering/fine-tuning-ag… Or if you’re in Rio, check out @Shopify’s booth at @iclr_conf to chat about it in person!

We reverse-engineered training data from thousands of merchant-created automations and fine-tuned Qwen3-32B into a tool-calling agent for Shopify Flow. Results: 2.2x faster, 68% cheaper The more interesting part: why we trained on Python instead of our own DSL, and what broke when benchmarks looked good but production didn't. ⬇️

Find the full 3-day schedule of our booth presentations here. ta-conference.myshopify.com/pages/shopify-…

Our SimGym Expo Talk is Saturday @ 12:45pm (room 201c) join the chat and find the recording (eventually!) here. iclr.cc/virtual/2026/e…

🇧🇷 Come talk shop with Shopify at ICLR 2026 in Rio starting Thursday! 🇧🇷 We're building ML that runs 10% of e-commerce — from LLM-powered agents to production retrieval systems and sim-to-real pipelines. Real problems, real scale. Redefining commerce intelligence. Stop by Booth #202 for deep dives on: ⚡️Sidekick — agentic merchant AI with MCP tools + multi-turn reasoning ⚡️ Commerce Foundation Model — cross-domain architecture for recs, search, catalog ⚡️ Global Catalog — multimodal LLMs, 10K+ categories, 40M inferences/day ⚡️ Search Rewriting — L1 retrieval + L2 LLM re-ranking at scale ⚡️ SimGym — sim-to-real agent training for storefronts (Expo Talk 📷) ⚡️ Tangle — the open source ml experimentation platform

The full story of the extension @davebcn87 & @tobi open-sourced is on our Eng Blog: shopify.engineering/autoresearch

Since we open-sourced pi-autoresearch, @Shopify teams have been running it on everything. Results so far: Unit tests: 300x faster React component mounting: 20% faster CI build time: 65% reduction Made pnpm run faster Autoresearch never stops trying things you'd never have time to try. Repo: github.com/davebcn87/pi-a…

.@fnthawar talks AI infrastructure, culture, and guardrails behind productivity gains with @bessemervp

Commerce data is inherently messy: inconsistent product images, descriptions in dozens of languages, millions of merchants all doing it differently. Most AI benchmarks don't capture that complexity. We partnered with @MLCommons to change that:

Insights that were once out of reach for smaller merchants are now available, fast. Full deep dive → shopify.engineering/simgym #GTC2026

SimGym now runs simulated shopping sessions by the hundreds of thousands daily—with a step-change in speed on Blackwell GPUs. We partnered with engineers from @NVIDIAAI and @vLLM_project to shape a new inference stack around real production traffic: custom FlashInfer kernels, speculative decoding, and async scheduling.

The results in production for a large GraphQL list query running breadth-first: 15x faster field-level execution, 6x less GC overhead, 4+ seconds off P50 end-to-end time. Here’s more: shopify.engineering/faster-breadth…

When we profiled large and slow GraphQL list queries at Shopify, we found I/O wasn't always the bottleneck. Frequently it was GraphQL’s conventional depth-first execution design. We tried executing breadth-first instead. It went… really well.