Engineering Manager, Ads ML Efficiency

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About the Role

Reddit is building a dedicated Ads ML Efficiency function to make model training and inference materially faster, cheaper, safer, and more scalable. As the Engineering Manager for this team, you will lead a group focused on model optimization, training efficiency, GPU enablement, load testing, model performance tooling, and efficiency guardrails across Ads ML.

This role sits at the intersection of ML modeling, systems optimization, and organizational leverage. You will partner closely with ranking teams, ML Platform teams and serving owners to identify the highest-value bottlenecks, land measurable efficiency wins, and build the tooling and operating mechanisms that make those wins repeatable.

What you’ll do:

• Lead & Grow: Hire, mentor, and retain a high-performing team of ML engineers / systems-oriented engineers working on model optimization and ML efficiency.
• Set Technical Direction: Define the roadmap for training optimization, inference optimization, launch-readiness tooling, and reusable efficiency primitives across Ads ML.
• Deliver Measurable Wins: Drive reductions in model training time, online latency, serving cost, and infra-driven launch risk.
• Build Systems and Tooling: Guide the development of profiling, benchmarking, load testing, observability, cost analysis, debugging, and efficiency certification systems.
• Operate in the Critical Path: Partner with model owners and platform teams to accelerate high-priority launches and remove bottlenecks from the path to production.
• Shape the Team’s Evolution: Balance near-term white-glove optimization work with medium-term platformization and automation.
• Build XFN Alignment: Work closely with MLP, AMP, Ranking, and serving teams to clarify boundaries, upstream generic wins, and keep Ads needs on track.
• Raise the Bar: Establish engineering rigor around measurement, performance debugging, launch safety, and technical decision-making for efficiency work.

What we’re looking for:

• Deep ML Engineering Experience: The candidate should have been close to the models themselves and understand training, serving, debugging, and optimization in depth.
• Hands-on Optimization Background: Direct experience improving training loops, serving systems, profiling workflows, model/inference efficiency, or GPU utilization.
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