Turn reality capture into complete deliverables faster. Mach9 automatically drafts features from scans at centimeter-level accuracy and infrastructure scale. Backed by Y Combinator.
About 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. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- 3+ years of work experience in relevant fields
- Bachelor's or Master's degree in Computer Science, Engineering, or equivalent experience
- Strong communication skills and the ability to work closely with ML researchers and engineers to understand their workflows and translate them into robust systems
- Experience designing and building data versioning, artifact management, or dataset lineage systems (e.g., DVC, LakeFS, Weights & Biases, or custom solutions)
- Hands-on experience with ML pipeline orchestration tools (e.g., Airflow, Prefect, Metaflow, or similar)
- Experience with model serving and inference optimization — profiling latency, reducing memory footprint, or scaling serving infrastructure to meet real-time constraints
More about this 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.
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...
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