Senior ML Performance Engineer - Ads, Remote

Reddit; United States; Remote; USD 217,000 - 303,000; Full time

Benefits offered by this job

Global Benefits

Family Planning

Mental Health

Pension with Employer match

Medical & Dental

Income Replacement

Bike to Work

Job summary

Reddit is seeking a Senior Machine Learning Engineer for Ads ML Efficiency. This role is fully remote within the United States.

You will own end-to-end performance optimizations for production ML workloads, profile bottlenecks, and implement improvements across inference, runtimes, memory, and hardware utilization.

Collaborate with model engineers and platform teams to deliver scalable, reliable systems, share benchmarks, and mentor engineers while maintaining production quality.

Qualifications

• Hands-on ML performance engineering experience in production workloads.
• Deep expertise in inference, runtimes, or GPU optimization.
• Strong profiling skills with reliable baselines.
• Solid Python and systems programming fundamentals.

Responsibilities

• Own end-to-end performance optimization initiatives for production ML workloads.
• Profile and diagnose ML bottlenecks across model execution, runtimes, memory and infrastructure.
• Design and implement performance improvements for inference, memory, precision, and kernels.
• Improve training/serving efficiency while maintaining model quality and reliability.
• Support faster model launches with benchmarking, load testing, and regression checks.
• Collaborate with model engineers and platform teams to deliver measurable end-to-end impact.
• Build reusable benchmarks, tooling, and guidance for future model development.

Skills

ML performance engineering

Profiling & benchmarking

Python

Systems programming

ML systems understanding

Education

Bachelor's degree in CS/EE

Tools

PyTorch

CUDA

TensorRT

Job description

Reddit is seeking a Senior Machine Learning Engineer for Ads ML Efficiency. This role is fully remote within the United States.

You will own end-to-end performance optimizations for production ML workloads, profile bottlenecks, and implement improvements across inference, runtimes, memory, and hardware utilization.

Collaborate with model engineers and platform teams to deliver scalable, reliable systems, share benchmarks, and mentor engineers while maintaining production quality.