Machine Learning Engineer — Real-Time Edge AI & Data Infra

Mach Industries; Los Angeles (CA); On-site; USD 170,000 - 230,000; Full time

Benefits offered by this job

Health insurance

Retirement plans

Professional development

Job summary

Mach Industries is looking for a Machine Learning Engineer to own and scale the training, data, and edge-inference backbone powering detection, tracking, and multi-sensor models across product lines.

You will build data/training infrastructure, generate synthetic data, and deploy research-grade models on embedded hardware in flight, collaborating across perception, localization, and flight-test teams to move capabilities from prototype to deployment.

Qualifications

• Strong generalist software engineering in Python and C++ on Linux.
• Experience building ML data and training pipelines end to end.
• Hands-on training and fine-tuning in PyTorch (CNN/Transformer).
• Edge/real-time deployment with latency and SWaP constraints.
• Data/MLOps infrastructure: dataset/versioning, CI validation, multi-GPU training.

Responsibilities

• Own and evolve training and data infrastructure for the autonomy team.
• Scale training/eval infrastructure with multi-GPU setups and model registry.
• Deploy and optimize models for real-time edge inference on Jetson-class hardware.
• Build and improve models across detection, segmentation, tracking, and fusion.
• Manage synthetic data generation to cover long-tail and sim-to-real gaps.

Skills

Python for ML

Production C++ on Linux

Profiling & optimization

Rigorous testing discipline

ML data pipelines

PyTorch

Education

BS/MS/PhD in CS/EE/Robotics

Tools

Docker

SageMaker

ROS 2

NVIDIA Jetson deployment pipelines

Parquet/DVC

Job description

Mach Industries is looking for a Machine Learning Engineer to own and scale the training, data, and edge-inference backbone powering detection, tracking, and multi-sensor models across product lines.

You will build data/training infrastructure, generate synthetic data, and deploy research-grade models on embedded hardware in flight, collaborating across perception, localization, and flight-test teams to move capabilities from prototype to deployment.