Machine Learning Engineer 5 - Decisioning & Optimization

• ML infrastructure for model serving: real-time inference at 1M+ QPS, multi-model parallel evaluation, feature hydration, model lifecycle from canary deployment through production monitoring
• Auction, ranking, and scoring: multi-stage candidate selection, scoring, bid valuation, dynamic pricing, and podding
• Budget, pacing, and bidding: control systems for delivery optimization, budget planning, and bid computation

We are scaling from a handful of production models to 10+ while maintaining sub-20ms P99 inference budgets. We are looking for an ML engineer who can build and operate the serving infrastructure these models run on, and who understands the ads decisioning context well enough to make the right engineering tradeoffs.