Staff Machine Learning Engineer, ML Efficiency

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.

Qualifications

Required

• BS, MS, or PhD in Computer Science or a related field.
• 5+ years of software engineering experience.
• Strong proficiency in Python
• Profiency 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.

Preferred

• 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.
• Experience with building real time ML inference applications

What Success Looks Like

• ML engineers can move from idea to experiment faster.
• Training and inference costs decrease, performance increases, while model quality is maintained or improved.
• GPU utilization and cluster efficiency increase.
• Platform reliability improves as ML workloads scale.
• Teams spend less time managing infrastructure and more time building models.
• Average recommendation model size increases.