Staff Machine Learning Engineer (Ads ML Efficiency)

Join Reddit as a Staff Machine Learning Engineer in the ML Efficiency team. You will design and build systems to improve the efficiency of ML training and inference workloads, develop tooling for ML engineers, and optimize resource utilization. You will partner with ML researchers and product teams to identify bottlenecks and drive performance improvements. Your success will be measured by increased developer productivity, reduced infrastructure costs, and improved model quality.

• Design and build systems that improve the efficiency of ML training and inference workloads, and develop tooling that helps ML engineers debug, profile, optimize, and monitor model performance.
• Partner with ML researchers and product teams to identify bottlenecks and drive performance improvements, and lead cross-functional initiatives that improve the productivity of Reddit ML engineers.
• Drive technical strategy for ML platform scalability, reliability, and cost efficiency, and build benchmarking frameworks and performance dashboards for training and serving systems.

• Strong proficiency in Python
• Profiency in at least one systems language (Go, C++, Rust, or Java) preferred
• Experience building distributed systems at scale
• Strong debugging and profiling skills
• Deep understanding of performance engineering and systems optimization
• 5+ years of software engineering experience
• BS, MS, or PhD in Computer Science or a related field
• Experience with machine learning infrastructure, training systems, or model serving platforms
• Experience with large-scale recommendation, ranking, generative AI, or foundation model systems
• Familiarity with GPU architectures and performance analysis tools
• Experience with distributed training frameworks such as PyTorch Distributed, Ray, Tensorflow, Spark
• Experience optimizing cloud infrastructure costs across large ML workloads
• Experience with building real time ML inference applications
• Contributions to internal platforms used by multiple ML teams