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NVIDIA

Designs the chips and software that power AI

Software Engineer - Performance Instrumentation

Software EngineeringConfirmed live in the last 24 hours
Full-timeIn-person

About the Job

Requirements

  • A bachelor's degree in Computer Science, Computer Engineering, or a related field, or equivalent experience.
  • At least 8 years of relevant software engineering experience, including hands-on experience with instrumentation or tracing in production machine learning or systems software.
  • Proficiency in CUDA and working knowledge of machine learning frameworks such as PyTorch or JAX, as well as annotation or tracing APIs such as NVTX.
  • Experience maintaining data quality in a pipeline, including validation, checks, alerting, and provenance.
  • Ability to measure instrumentation overhead and make trade-offs among overhead, fidelity, sampling, and aggregation.
  • Experience implementing modifications in code managed by other groups by identifying requirements, reaching consensus on interfaces, and ensuring completion through delivery.
  • Strong debugging skills across component boundaries and ability to validate changes through code review, tests, and independent measurements, including using automated development tools.
  • Proficiency in Python and C++.

Responsibilities

  • Define and maintain performance data requirements from sources including profiling libraries, annotation APIs, frameworks, runtimes, communication libraries, schedulers, configurations, and infrastructure telemetry.
  • Identify and demonstrate visibility gaps with concrete evidence, showing which diagnoses are not possible with current data, then collaborate with relevant teams to address the gaps or build necessary solutions.
  • Inform the development of low-overhead instrumentation and annotation APIs from the consumer perspective, ensuring necessary information is conveyed efficiently and effectively.
  • Ensure data quality at ingestion by implementing validation, sanitization, checks, and alerting mechanisms to detect gaps or errors before they affect users or automated analysis.
  • Measure and manage the overhead and observer effects of instrumentation on real workloads, keeping them within acceptable limits.
  • Collaborate with multiple partner teams simultaneously, manage commitments, and ensure continuous progress.

Desired Qualifications

  • Experience establishing telemetry conventions or schemas adopted by other teams.
  • Background in storage, checkpointing, or data-loading performance.
  • Expertise in crafting low-overhead event collection.
  • Familiarity with network and collective-communication telemetry, including NCCL, NICs, and fabric counters.

About the Company

NVIDIA

Designs the chips and software that power AI

NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
1993

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