Evaluating, explaining, and improving deep learning workloads for both training and inference, contributing to advancements in throughput, latency, and efficiency across NVIDIA GPU platforms.
Collaborating across NVIDIA with researchers, engineers, and hardware specialists to recognize bottlenecks and achieve performance improvements.
Developing production-quality software across the deep learning platform stack, from frameworks to deployment.
Building automation and diagnostics that enable reproducible, scalable, and backend-agnostic performance improvements.

Requirements

Pursuing or recently completed a Master’s or PhD in Computer Science, Electrical Engineering, Computer Engineering, or a related field (or equivalent experience).
Relevant experience in deep learning, high-performance computing, or related fields.
Extensive background in improving deep learning workloads, showcasing a deep understanding of training and inference constraints.
Proven ability in GPU performance analysis and profiling, with hands-on experience applying advanced optimization techniques.
Solid knowledge of computer architecture and familiarity with the fundamentals of GPU development.
Strong programming skills in Python and C++.

Benefits

equity
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Applicant Tracking System Keywords

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Hard Skills & Tools

deep learninghigh-performance computingGPU performance analysisprofilingoptimization techniquescomputer architecturePythonC++

Soft Skills

collaborationproblem-solving

Certifications

Master’s degreePhD