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AI GPU Memory Profiling Tool Developer

Lumio Systems

Remote · Under $60k / year · Full-time · Posted 5 months ago

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About the role

GPU out-of-memory errors are the most frustrating part of ML development. The error message says "CUDA out of memory" but doesn't tell you where your 80GB went — is it model weights, activations, optimizer state, or a memory leak in your data loader? Our profiling tool provides real-time GPU memory breakdowns that answer these questions. As a junior developer, you'll work on the data collection and presentation layers of the memory profiler. That means hooking into PyTorch's memory allocation APIs, aggregating allocation events into meaningful categories, and building the dashboards that show ML engineers exactly where their GPU memory is being consumed. The senior engineer handles the low-level CUDA instrumentation; you handle everything above the allocation event stream. The MLOps tools team is four engineers. You'll pair with the senior GPU tooling engineer for your first two months, learning how PyTorch manages memory internally. By month three, you'll own the memory categorization module that turns raw allocation events into the human-readable breakdowns our users see.

Fully remote.

Responsibilities

  • Build data collection from PyTorch memory allocation APIs
  • Categorize memory allocations into model weights, activations, optimizer state
  • Create dashboards showing real-time GPU memory breakdowns
  • Detect memory leak patterns from allocation event streams
  • Write tests for categorization accuracy against known model architectures
  • Document memory profiling methodology for user guides

Requirements

  • 1+ year of Python development experience
  • Basic understanding of PyTorch and GPU computing concepts
  • Interest in developer tooling and performance analysis
  • Familiarity with Docker for local development
  • Systematic approach to debugging and data analysis

Benefits

  • Fully remote with flexible schedule
  • Health and dental insurance
  • $1,500 learning budget
  • Mentorship from senior GPU systems engineer
  • Build tools the ML community actually uses

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Categories

MLOps & AI Infrastructure AI & Machine Learning Engineers

Skills

python pytorch docker kubernetes aws

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Job Type

Full-time

Level

Junior

Language

English

Salary Range

Under $60k / year

AI Expertise

MLOps & AI Infrastructure AI & Machine Learning Engineers

Skills

python pytorch docker kubernetes aws

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