Reddit Staff Machine Learning Engineer at Reddit focused on building infrastructure and tooling to improve ML training and inference efficiency. Responsibilities include system design, performance optimization, and cross-functional leadership.
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.
Qualification
BSStrong proficiency in PythonStrong debugging and profiling skillsWhat Success Looks Like
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.
