Staff Machine Learning Engineer, Ads ML Efficiency

Reddit is hiring a Staff Machine Learning Engineer for its ML Efficiency team. The role builds infrastructure, tooling, and optimization systems to improve the efficiency, productivity, and cost effectiveness of machine learning training and inference at scale.

Responsibilities:

• Design and build systems that improve the efficiency of ML training and inference workloads
• Develop tooling that helps ML engineers debug, profile, optimize, and monitor model performance
• Improve GPU and general resource utilization through scheduling, resource management, caching, and workload optimization
• Partner with ML researchers and product teams to identify bottlenecks and drive performance improvements
• Build benchmarking frameworks and performance dashboards for training and serving systems
• Optimize distributed training infrastructure, data pipelines, and model serving architectures
• Lead cross-functional initiatives that improve the productivity of Reddit ML engineers
• Drive technical strategy for ML platform scalability, reliability, and cost efficiency

Requirements:

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