Mach9
ML Infrastructure Engineer
San Francisco · Mid
Sponsorship not specifiedDetected 88 days ago
PythonAlgorithmsAWSTerraformCI/CDMachine LearningAirflowData EngineeringNLPComputer VisionCommunication
About the role
- 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.
- 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.
- 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 monitoring.
- At Mach9, ML infrastructure engineers build and maintain the systems that power production AI models for civil engineering and surveying.
Requirements
- 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.
- 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.
Nice to have
- Familiarity with AWS infrastructure services.
- Experience with containerized ML workflows and GPU-accelerated training environments.
- Experience with model optimization techniques (e.g., quantization, TensorRT, ONNX Runtime, distillation).
- Knowledge of infrastructure-as-code tools (e.g., AWS CDK, Terraform).
This listing is sourced directly from Mach9's careers page and normalized into a canonical job model.