Engineering Manager, Ads ML Efficiency

Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 126 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com. Reddit has a flexible workforce! If you happen to live close to one of our physical office locations our doors are open for you to come into the office as often as you'd like. Don't live near one of our offices? No worries: You can apply to work remotely in any country in which we have a physical presence. 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.