1 month ago

Staff Machine Learning Engineer, ML Efficiency

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Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 16 hours agoStatus: Live

Job Summary

Design and optimize the ML efficiency platform by building scalable systems to accelerate training and inference, develop debugging and profiling tooling, and improve GPU/resource utilization. Collaborate with researchers and product teams to identify bottlenecks, create benchmarking dashboards, and lead cross-functional initiatives to enhance Reddit’s ML infrastructure, reliability, and cost efficiency.

Location: Netherlands

Workplace: Remote

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key 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.

Key Requirements

•BS, MS, or PhD in Computer Science or a related field.

Experience:5+ yearsMachine learningDistributed systemsHigh-performance computing

Education:Bachelor's

Skills:CommunicationProblem-solvingCollaborationAttention to detailTeamwork

Languages:English

Tech Stack:PythonGoC++RustJavaPyTorchTensorFlowRaySparkGPUKubernetesDockerDistributed systemsCloud

Company Brief

Operates Reddit, a large online community and discussion platform where users submit content, comment, and vote across topic-based communities (subreddits); monetizes via advertising, premium subscriptions, and awards.

Industry: Digital Media

Company Size: Enterprise (1,001+ employees)

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: San Francisco, United States

Founded: 2005

Website LinkedIn

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