Staff Machine Learning Engineer, Ads ML Efficiency

This is a fully remote position, open to applicants in United States, +1 more country.

📋 Description

• Design and develop systems that enhance the efficiency of machine learning training and inference processes.

• Create tools that assist ML engineers in debugging, profiling, optimizing, and monitoring model performance.

• Enhance GPU and overall resource utilization through effective scheduling, resource management, caching, and optimization of workloads.

• Collaborate with ML researchers and product teams to pinpoint bottlenecks and implement performance enhancements.

• Construct benchmarking frameworks and performance dashboards for both training and serving systems.

• Optimize distributed training infrastructure, data pipelines, and architectures for model serving.

• Lead cross-functional projects that boost the productivity of Reddit's ML engineers.

• Advocate for technical strategies that enhance the scalability, reliability, and cost-effectiveness of the ML platform.

• Empower ML engineers to transition from ideas to experiments more rapidly.

• Reduce training and inference costs while enhancing performance and ensuring or improving model quality.

• Increase GPU utilization and overall cluster efficiency.

• Enhance platform reliability as ML workloads grow.

⛳️ Requirements

• Bachelor's, Master's, or PhD degree in Computer Science or a related discipline.

• Over 5 years of experience in software engineering.

• Strong expertise in Python programming.

• Proficiency in at least one systems programming language (preferably Go, C++, Rust, or Java).

• Experience in building distributed systems at scale.

• Familiarity with machine learning infrastructure, training systems, or model serving platforms.

• In-depth knowledge of performance engineering and systems optimization techniques.

• Excellent debugging and profiling capabilities.

• Preferred: experience with large-scale recommendation, ranking, generative AI, or foundational model systems.

• Preferred: experience with distributed training frameworks such as PyTorch Distributed, Ray, TensorFlow, or Spark.

• Preferred: understanding of GPU architectures and performance analysis tools.

• Preferred: experience in optimizing cloud infrastructure costs across extensive ML workloads.

• Preferred: contributions to internal platforms utilized by multiple ML teams.

• Preferred: experience in developing real-time ML inference applications.

🏝️ Benefits

• Global benefit programs tailored to your lifestyle, including workspace, professional development, and caregiving support.

• Family Planning Support.

• Gender-Affirming Care.

• Mental Health & Coaching Benefits.

• Group Personal Pension Scheme with Employer match.

• Private Medical and Dental Scheme.

• Income Replacement Programs.

• Bike to Work scheme.

• Flexible Vacation & Paid Volunteer Time Off.

• Generous Paid Parental Leave.

• Equity in the form of restricted stock units.

• Medical, dental, and vision insurance.

• 401(k) program with employer match.

• Generous time off for vacation and parental leave.