ML Infrastructure Engineer

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Verification

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Salary

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401(k) match

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Work mode

Onsite From the posting source checked Jun 20, 2026

In-office days

5 days From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Company

Company stage

Seed From the posting source checked Jun 20, 2026

Application

Cover letter

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Assessment

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Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

ML Infrastructure Engineer San Francisco | OnSite THE ROLE At Mach9, ML infrastructure engineers build and maintain the systems that power production AI models for civil engineering and surveying. Our ML pipeline spans 10,000+ miles of labeled survey data, image segmentation networks, and 3D prediction models serving real-time inference to surveyors and engineers in the field. This role is ideal for mid-career ML infrastructure engineers with experience building for both training and inference. You'll build training pipelines that handle deep transformer models on hundreds of terabytes of 3D point cloud and image data. You'll also architect our inference infrastructure, delivering both heavy offline detection algorithms and real-time responsive inference that integrates directly with our CAD software. RESPONSIBILITIES - Design and build a centralized system for versioning training data, generated datasets, and model artifacts, with full lineage tracking from raw source data through to trained model outputs. - Develop and maintain reliable, reproducible ML training and data generation pipelines. - Refactor and harden existing training and data generation scripts into composable, testable, and maintainable components. - Create CI/CD workflows for validating data pipelines and model training runs, including automated correctness checks and regression detection. - Build tooling that enables ML engineers to launch, monitor, and debug training jobs with minimal friction. - Optimize and scale real-time model inference services to meet latency and throughput requirements in production, including profiling, batching strategies, and resource-efficient serving. - Own the deployment path from trained model artifact to production endpoint, ensuring reliable rollouts, rollback, and

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