Reddit

Overview

The ML Efficiency team builds the infrastructure, tooling, and optimization systems that enable machine learning engineers and researchers to train, evaluate, deploy, and operate models efficiently at scale. We focus on improving developer productivity, reducing infrastructure costs, increasing hardware utilization, and accelerating experimentation across the company’s ML ecosystem.

About Reddit

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

Requirements & Eligibility

• BS, MS, or PhD in Computer Science or a related field.
• 5+ years of software engineering experience.
• Strong proficiency in Python
• Proficiency in at least one systems language (Go, C++, Rust, or Java) preferred
• 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.

Key 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.