Manager
Lead Reddit's new Ads ML Efficiency team — training/inference optimization, GPU enablement, and launch tooling
Reddit is standing up a dedicated Ads ML Efficiency function and hiring an Engineering Manager (M2) to lead it. You'll manage ML/systems engineers focused on training optimization, GPU enablement, load testing, and efficiency guardrails, partnering closely with Ranking, ML Platform, and serving teams to land measurable cost/latency wins across Ads ML.
Step into this interview
Free · a live voice mock calibrated to this exact role
What this interview tests
- Standing up and prioritizing roadmap for a new ML efficiency function
- Hands-on depth in training/inference optimization, profiling, and GPU utilization
- Hiring, coaching, and retaining ML/systems-hybrid engineers
- Balancing bespoke optimization work vs. building reusable platform tooling
- Cross-functional alignment with Ranking, ML Platform, and serving teams
Common question themes
Prioritizing the first bottleneck to tackle when standing up a new team
A concrete story of speeding up a training loop or serving system with measurable wins
Building profiling, benchmarking, or launch-certification tooling
Hiring and mentoring engineers who blend ML modeling and systems skills
Resolving cross-team ownership conflicts with Ranking/MLP/serving teams
Explaining a technical tradeoff (cost, latency, accuracy) to senior stakeholders
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