Software Engineer, ML Performance

NVIDIA

. Analyze performance of distributed training and inference workloads across the entire stack .

Posted 10/31/2026full-timeUnited StatesSeniorLead💰 $224,000 - $356,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Expertise in analyzing and optimizing GPU training and inference workloads, with a strong focus on performance metrics and debugging across multiple layers of the stack. Proven ability to communicate findings effectively and drive improvements through collaboration with engineering teams.

Highest-signal resume keywords

GPU OptimizationPerformance AnalysisPython ProficiencyCUDA ProficiencySoftware Engineering Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Performance Metrics DefinitionRegression AnalysisDebugging SkillsMaintainable Analysis CodeAutomated Diagnoses

Soft Skills

Clear CommunicationProblem-Solving

Tools & Technologies

Framework InterfacesRuntime InterfacesCompiler Interfaces

Industry Keywords

Distributed TrainingInference WorkloadsPerformance BottlenecksProduction Environments

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Analyze performance of distributed training and inference workloads across the entire stack
Identify bottlenecks, resolve them or suggest solutions, and provide supporting evidence
Define performance metrics tracked across a fleet of GPUs
Break metrics down into factors engineers need to debug and prioritize platform signals
Drive regression analysis across machines, versions, and configurations
Evaluate automated diagnoses and build labeled cases for analysis validation
Work with build partners on real workloads and carry findings through to improvements
Feed product gaps into the roadmap

Requirements

What you’ll need

Bachelor’s degree in Computer Science, Computer Engineering, or a related field, or equivalent experience
12+ years of relevant software engineering experience
Hands-on optimization of GPU training or inference workloads in production
Performance work across more than one layer of the stack
Experience diagnosing performance across many jobs, machines or tenants
Familiarity with framework, runtime and compiler interfaces
Strong programming and debugging skills
Maintainable analysis code, tests, or repeatable investigation procedures
Clear communication of findings, trade-offs, and remaining uncertainty
Proficiency with Python and CUDA

Benefits

Comp & perks

Equity
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