Staff Machine Learning Engineer, ML Efficiency

5+ years of software engineering experience.

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.

Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support

Family Planning Support

Gender-Affirming Care

Mental Health & Coaching Benefits

Private Pension plan with Employer-matching

100% employer-sponsored group medical plan

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.

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.