Staff ML Engineer, ML Efficiency (Remote)

Reddit, Inc.; Northern (KY); Hybrid; USD 230,000 - 322,000; Full time

Benefits offered by this job

Equity RSUs

401(k) match

Private Medical

Dental

Mental Health

Parental Leave

Bike to Work

Job summary

Reddit is seeking a Staff Machine Learning Engineer focused on Ads ML Efficiency. This remote-friendly role requires deep expertise in ML infrastructure, performance engineering, and system optimization to accelerate training, evaluation, and deployment of models at scale.

You will design systems, build tooling for debugging and profiling, and partner with researchers and product teams to drive performance improvements across the ML platform.

Qualifications

• BS/MS/PhD in Computer Science or related field.
• 5+ years of software engineering experience.
• Strong proficiency in Python.
• Experience with ML infrastructure, training systems, or model serving platforms.
• Deep understanding of performance engineering and systems optimization.
• Strong debugging and profiling skills.

Responsibilities

• Design and build systems that improve ML training and inference efficiency.
• Develop tooling to debug, profile, and monitor model performance.
• Improve GPU and resource utilization via scheduling and caching.
• Partner with ML researchers and product teams to identify bottlenecks.
• Build benchmarking frameworks and performance dashboards for training and serving.
• Optimize distributed training infrastructure and data pipelines.
• Lead cross-functional initiatives to boost Reddit ML productivity.
• Drive technical strategy for ML platform scalability and cost efficiency.

Skills

Python

Go

C++

Rust

Java

ML infrastructure

Performance engineering

Debugging

Profiling

Education

BS in Computer Science

MS in Computer Science

PhD in Computer Science

Tools

PyTorch Distributed

Ray

TensorFlow

Spark

Job description

Reddit is seeking a Staff Machine Learning Engineer focused on Ads ML Efficiency. This remote-friendly role requires deep expertise in ML infrastructure, performance engineering, and system optimization to accelerate training, evaluation, and deployment of models at scale.

You will design systems, build tooling for debugging and profiling, and partner with researchers and product teams to drive performance improvements across the ML platform.