Staff Machine Learning Engineer, Ads Creative Effectiveness

Remote - United Statesfull timeRemote

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senior

$230k–$322k

H1B: yes

Reddit Staff Machine Learning Engineer on the ML Efficiency team at Reddit focusing on building systems to improve ML training and inference efficiency. Responsible for developing tooling, optimizing resource utilization, and driving technical strategy for ML platform scalability and cost 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.

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.