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AI Model Performance Profiling Tool Developer

Joltura Labs

Remote · $100k – $130k / year · Full-time · Posted 5 months ago

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About the role

ML engineers know their model is slow but can't tell you why. Is it the attention layers? The tokenizer? The data loading? The GPU memory thrashing? Our profiling tool instruments the full inference path — from API request to GPU kernel execution — and produces flamegraph-style visualizations that pinpoint exactly where time is spent. The tool needs to work across model types (transformers, CNNs, GBMs), serving frameworks (Triton, vLLM, TorchServe), and deployment environments (Kubernetes, bare metal, serverless). You'll build the instrumentation layer that hooks into these different stacks, the aggregation pipeline that collects profiling data without significantly impacting inference latency, and the visualization frontend that makes bottlenecks obvious. The developer tools team is four engineers. You'll own the profiling backend — instrumentation, data collection, and analysis. The frontend developer handles visualization; the platform engineers handle integration with our deployment system. Every improvement gets measured by how fast users can identify their inference bottleneck.

Fully remote.

Responsibilities

  • Build instrumentation for profiling across model types and serving frameworks
  • Design low-overhead data collection that doesn't impact inference latency
  • Create analysis pipelines that identify bottlenecks from profiling data
  • Support multiple deployment environments: Kubernetes, bare metal, serverless
  • Develop profiling APIs used by the visualization frontend
  • Test profiling accuracy against known synthetic bottlenecks

Requirements

  • 3+ years in systems engineering with profiling or observability experience
  • Understanding of GPU execution, CUDA, and model inference pipelines
  • Experience with PyTorch internals or model serving frameworks
  • Strong Python skills with performance measurement methodology
  • Familiarity with Kubernetes and container-based deployments

Benefits

  • Fully remote with async communication
  • Health, dental, and vision insurance
  • $3,000 annual learning budget
  • Stock options
  • Build tools used by ML engineers daily

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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

Mid-Level

Language

English

Salary Range

$100k – $130k / year

AI Expertise

MLOps & AI Infrastructure AI & Machine Learning Engineers

Skills

python pytorch docker kubernetes aws mlflow

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