ML Infra Architect — Real-Time AI Pipelines & Scale

Mach9

San Francisco (CA)

On-site

USD 150,000 - 210,000

Full time

14 days+
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Benefits offered by this job

Competitive salary
Health insurance
Flexible hours
Equity

Job summary

Mach9 is seeking a mid-career ML infrastructure engineer to design and maintain systems powering production AI for civil engineering and surveying. You’ll build training pipelines on large 3D point cloud and image data and architect real-time inference that integrates with CAD software.

You will collaborate with ML researchers and engineers to deliver reliable, scalable pipelines, robust data/versioning, and efficient model serving in a production environment.

Qualifications

  • 3+ years of work experience in ML infrastructure.
  • Bachelor's or Master's degree in CS/Engineering or equivalent.
  • Strong communication and collaboration with ML researchers.
  • Experience with data versioning or artifact management.
  • Hands-on with model serving and inference optimization.

Responsibilities

  • Design and build a centralized system for data and model lineage.
  • Develop reliable ML training and data-generation pipelines.
  • Refactor scripts into maintainable, testable components.
  • Create CI/CD workflows for data pipelines and training runs.
  • Build tooling to launch, monitor, and debug training jobs.
  • Scale real-time inference services to meet latency requirements.

Skills

Python
PyTorch
Data pipelines
CI/CD
Communication

Education

Bachelor's or Master's in Computer Science or Engineering

Tools

Airflow
Prefect
Metaflow

Job description

Mach9 is seeking a mid-career ML infrastructure engineer to design and maintain systems powering production AI for civil engineering and surveying. You’ll build training pipelines on large 3D point cloud and image data and architect real-time inference that integrates with CAD software.

You will collaborate with ML researchers and engineers to deliver reliable, scalable pipelines, robust data/versioning, and efficient model serving in a production environment.

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