This episode of The Experimentation Edge unpacks how DoorDash's experimentation platform runs 12,000+ A/B tests per year across 42 million monthly active users — and now powers merchant-led testing on menu pricing and promotions. Ilya Izrailevsky, Senior Engineering Manager leading the platform, explains how feature flags, marketplace experimentation, and CEO-level experiment reviews built a multi-million-dollar experimentation culture across consumers, dashers, and merchants.

Summary
Most companies struggle to scale experimentation beyond engineering teams. DoorDash runs over 12,000 experiments per year across 42 million monthly active users — and now they're enabling restaurant owners to run their own A/B tests on menu pricing and promotions. Ilya Izrailevsky, Senior Engineering Manager leading DoorDash's experimentation platform, shares how the company built a three-sided marketplace testing program that balances consumers, dashers, and merchants across 40+ countries. From his time scaling search at Amazon (where offline model evaluation narrowed hundreds of candidates down to 10 for live testing) to preventing DashPass churn at DoorDash, Ilya reveals what happens when experimentation scales beyond product teams — and why CEO-level experiment review emails drive cultural change faster than any training program.

One standout learning: expanding delivery radius to 11+ miles increased grocery orders but tanked retail conversions. The lesson wasn't about distance — it was that one metric approach breaks in multi-dimensional marketplaces. DoorDash now segments experimentation by vertical, behavior pattern, and regional market, using AI agents to mine institutional knowledge from past tests and auto-generate experiment summaries that ship company-wide within hours of readout.

Timestamps
00:40 From building Wasabi (Intuit's open-source platform) to running ML at Amazon and Uber 
03:04 Why product velocity without experimentation creates feature bloat, not impact 
05:32 Scaling search at Amazon: billions of products, 10 visible results, 25% win rate 
08:22 Offline evaluation as a filter — golden data sets cut model candidates before live traffic 
10:23 DoorDash's three-sided marketplace: 300 million feature flag evaluations per second 
12:38 CEO Tony Xu reads every experiment email and replies with alternative hypotheses 
13:33 Democratization at scale: enabling merchants to A/B test menu pricing and promotions 
17:05 DashPass churn experiment uncovered value perception gap — became a full product area 
22:03 Why expanding delivery radius killed retail orders but boosted grocery conversions 
24:16 No single North Star metric — balancing consumer quality, dasher earnings, merchant mix 
27:29 Four-dimensional scale: democratization, global expansion, new verticals, AI agents 
31:03 Agentic experimentation: AI mines past tests to generate hypotheses and debug imbalance

Takeaways
- Win rate matters less than learnings per test — DoorDash ships company-wide experiment summaries (win or lose) that the CEO actively reads and responds to, creating cultural accountability around testing rigor.
- Offline evaluation acts as a pre-filter for model velocity — Amazon's search team used golden data sets to cut hundreds of ML candidates down to 10 for live A/B testing, preventing wasted experiment slots.
- One-size metrics break in multi-dimensional marketplaces — DoorDash balances consumer retention, dasher utilization, and merchant inventory mix across verticals because optimizing one side degrades the ecosystem.
- Democratization requires opinionated templates, not open-ended tools — enabling non-technical users to run tests means embedding success metrics and guardrails into pre-built experiment configs.
- AI scales institutional knowledge, not just analysis speed — mining past experiment readouts to auto-generate new hypotheses turns your testing history into a compounding advantage.

Connect with the guest
LinkedIn: https://www.linkedin.com/in/ilyaizrailevsky/
Learn more about DoorDash: https://www.doordash.com/

Sponsor
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Topics: A/B testing, experimentation platform, feature flags, marketplace experimentation, machine learning, growth experimentation, statistical significance, experimentation culture, agentic AI workflows.

• (00:40) - From building Wasabi (Intuit's open-source platform) to running ML at Amazon and Uber