Senior Machine Learning Engineer, 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 . 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. This person will be a key senior engineer on that team, owning meaningful efficiency work across training systems, inference and serving paths, launch-readiness tooling, and reusable optimization capabilities for Ads ML. This role sits at the intersection of ML modeling, systems optimization, and engineering leverage. The engineer will partner closely with ranking teams, serving owners, and ML Platform to identify important bottlenecks, land measurable efficiency wins, and help build the mechanisms that make those wins repeatable. What you’ll do Independently own high-value optimization initiatives across training, inference, or launch-readiness for important Ads ML workloads. Diagnose bottlenecks in real production systems using profiling, benchmarking, and observability rather than intuition-first debugging. Build performance tooling, optimization playbooks, observability hooks, guardrails, or efficiency primitives that help more than one team or workload over time. Improve launch-safety and efficiency readiness by contributing to load testing, fallback readiness, latency