Senior Machine Learning Engineer, Ads Efficiency at Reddit

Confirmed open

Optimize Reddit advertising ML workloads for latency, throughput, hardware use and cost by profiling models, improving execution and validating production rollouts. The posting specifies remote work in the US or Canada.

Reddit

Location: Remote
Work arrangement: remote
Technologies: Python, PyTorch

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Role overview

Reddit’s Ads ML Efficiency team improves the speed and scalability of production machine-learning workloads. The employer lists remote work in the United States and Canada.

Responsibilities

Profile bottlenecks across runtimes, memory, CPU and GPU use; improve inference, training and serving through batching, precision, compiler integration or custom operators.
Develop reliable benchmarks, load tests and regression checks, measure production impact and work with ranking, serving and platform teams on safe rollouts.

Qualifications

Requires hands-on production ML performance engineering, systematic profiling and benchmarking, strong software fundamentals and Python plus relevant systems technologies.
Experience with PyTorch, inference runtimes, GPU optimization or distributed training is useful, but the posting emphasizes measurable performance work over any one framework.

Technology in this role

Python: Requires hands-on production ML performance engineering, systematic profiling and benchmarking, strong software fundamentals and Python plus relevant systems technologies.
PyTorch: Experience with PyTorch, inference runtimes, GPU optimization or distributed training is useful, but the posting emphasizes measurable performance work over any one framework.

Benefits

Reddit lists healthcare, retirement support, flexible vacation, parental leave and professional-development benefits. The US base-salary range in the posting is $216,700–$303,400.

About Reddit

Reddit is a community platform with more than 100,000 active communities. Its machine-learning teams support discovery, relevance, advertising and marketplace experiences.

How to apply

Review the original posting and apply through the employer’s careers page.

View source and apply

Source checked: 2026-10-27

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