# ML Infrastructure Engineer

**Company:** [Mach9](https://hotfix.jobs/companies/mach9)
**Location:** San Francisco, CA
**Role:** ML Engineering
**Salary:** $160k – $200k/yr
**Experience:** 3+ years
**Skills:** Python, PyTorch, Airflow, Prefect, Metaflow, Dvc, Lakefs, Weights & Biases, AWS, TensorRT, Onnx Runtime, Aws Cdk, Terraform
**Posted:** 2026-04-25

> Builds and maintains ML infrastructure for training pipelines handling massive 3D data and real-time inference serving integrated with CAD software. Requires 3+ years experience with Python, PyTorch, ML orchestration tools, data versioning, and inference optimization.

## Job Description

## 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 monitoring.

## 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.
- 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.
- Ability to read and refactor ML training code — you don't need to design model architectures, but you need to understand what training pipelines are doing well enough to make them reliable.
- Proficient with **Python**, **PyTorch**.

## Bonus Qualifications
- 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**).
- Experience building or operating ML systems that handle large unstructured datasets (imagery, 3D data, sensor data).

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