Senior Machine Learning Engineer, Ads ML Efficiency

About this role

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 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com .

Location: Reddit has a flexible first workforce. Don't live near our office? No worries: you can work remotely from anywhere in the US or Canada. About the Team Reddit's Ads ML Efficiency team is focused on making machine learning workloads faster, more scalable, and more cost-efficient, enabling our Ads ML teams to develop, launch, and scale increasingly sophisticated models. This role sits at the intersection of ML workloads, execution systems, and engineering leverage.

The engineer will partner closely with ranking teams, serving owners, and ML Platform to identify important bottlenecks, implement targeted improvements, and connect execution and systems improvements to measurable end-to-end outcomes. What you’ll do Own end-to-end performance optimization initiatives for high-impact production ML workloads, from bottleneck identification and implementation through validation and production rollout.

Profile and diagnose ML performance bottlenecks across model execution, frameworks, runtimes, memory, CPU/GPU utilization, and infrastructure using rigorous benchmarking and measurement. Design and implement performance improvements involving inference execution, runtime behavior, batching, memory management, precision, compiler integration, custom operators, kernels, or other relevant techniques. Improve ML training and serving efficiency and scalability, optimizing latency, throughput, hardware utilization, and cost while maintaining model quality and production reliability.

Support faster and safer model launches through performance benchmarking, load testing, regression detection, and launch-readiness improvements. Partner with model engineers and platform teams to identify high-leverage opportunities, integrate optimizations safely, and validate measurable end-to-end impact. Build reusable performance capabilities, including benchmarks, tooling, optimization patterns, and engineering guidance that accelerate future model development and deployment.