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AI Performance Benchmarking Engineer

Mia Levy

Contract · Job · Los_Angeles

About the role

When a client asks 'which GPU instance should I use for this model?' — we give them a data-driven answer instead of a guess. Our benchmarking platform profiles ML workloads across every major cloud GPU offering, measuring throughput, latency, cost-per-inference, and memory efficiency. This is a 6-month contract to expand our benchmark coverage. You'll profile 50+ model architectures across 20+ GPU instance types, build automated benchmarking pipelines, and create the comparison dashboards that help clients make infrastructure decisions. The work is systematic and rigorous. Each benchmark run needs to be reproducible, statistically valid, and documented. If you enjoy the intersection of ML and systems engineering — and you care about measurement precision — this is uniquely satisfying work.

Responsibilities

  • Profile ML model performance across 20+ cloud GPU instance types
  • Build automated benchmarking pipelines for throughput and latency measurement
  • Create comparison dashboards for client infrastructure decision-making
  • Ensure benchmark reproducibility and statistical validity
  • Document methodology and maintain benchmark result databases

Requirements

  • 3+ years in ML engineering or systems performance profiling
  • Strong Python and PyTorch skills for model loading and inference
  • Experience with Docker and Kubernetes for reproducible environments
  • Familiarity with AWS and GCP GPU instance offerings
  • Systematic approach to measurement and documentation

Benefits

  • 6-month contract with renewal option
  • Fully remote
  • Competitive contract rate
  • Access to diverse GPU hardware for benchmarking

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Categories

MLOps & AI Infrastructure AI & Machine Learning Engineers

Skills

python pytorch docker kubernetes aws

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Job Type

Contract

Level

Mid-Level

Language

English

Salary Range

$80k – $100k / year

AI Expertise

MLOps & AI Infrastructure AI & Machine Learning Engineers

Skills

python pytorch docker kubernetes aws gcp

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