# Machine Learning Engineer at Mach Industries

- Company: Mach Industries
- What the company does: Mach Industries is building faster, smarter defense infrastructure for the modern era. Backed by Khosla and Sequoia.
- Company website: https://www.machindustries.com/
- Type: Startups (AI role)
- Level: Mid level
- Location: Huntington Beach, California, United States; San Francisco, California, United States
- Work setup: On-site
- Posted: 2026-09-02
- Apply by: 2026-10-17
- Apply: https://job-boards.greenhouse.io/machindustries/jobs/4387877009
- Page: https://www.1752.vc/careers/jobs/mach-industries-machine-learning-engineer/

## About the role

Mach Industries is building an AI-forward autonomy stack for contested environments where GPS and other sensing are unavailable or unreliable. As a Machine Learning Engineer, you will own and scale the training, data, and edge-inference backbone that every vision and multi-sensor model on our product lines depends on for detection, tracking, search, navigation, targeting, and automatic target recognition.

## What they're looking for

- Strong generalist software engineering: Python for ML and tooling, plus production C++ on Linux, profiling, optimization, and rigorous testing discipline
- Proven experience building ML data and training pipelines end to end: dataset construction, labeling/QA, augmentation, experiment tracking, and reproducible training
- Hands-on training and fine-tuning in PyTorch across modern detection/segmentation/tracking architectures (CNN/Transformer)
- Edge and real-time deployment: model compression (INT8/FP16), runtime optimization (TensorRT/ONNX Runtime), and meeting latency/SWaP constraints on embedded GPU (Jetson-class) hardware
- Data and MLOps infrastructure: SQL/Parquet, dataset/versioning tools, CI-based validation, and scalable multi-GPU training
- BS/MS/PhD in CS/EE/Robotics or similar, or equivalent experience, with a track record shipping ML models to production or hardware. Senior candidates: deeper ownership of training/data infrastructure at scale

Tags: Software Engineering