Aerial Image ML Engineer — Remote, Equity & Growth

Raad Labs; California (MO); Hybrid; USD 180,000 - 230,000; Full time

Benefits offered by this job

Competitive equity

Health, dental & vision

Remote-first

Home office budget

Generous paid time off

Learning & travel budget

Job summary

Raad Labs is seeking a Machine Learning Engineer for aerial image classification. The role focuses on building and shipping detection and classification models across high-resolution aerial imagery, with emphasis on production-grade PyTorch pipelines and scalable deployment.

You will own dataset curation, augmentation strategies, and training infrastructure, plus deployment to clusters and edge devices. The position offers remote-first work from anywhere in the U.S. and a strong equity package.

Qualifications

• 4+ years building and shipping computer vision models to production.
• Experience with overhead or industrial imagery is preferred.
• Deep, practical PyTorch experience and fluency in modern detection literature and toolchain.
• Strong Python engineering habits; training code should be production-grade.
• Experience with deployment optimization (TensorRT, ONNX Runtime, quantization) is a strong plus.

Responsibilities

• Train, evaluate, and ship detection and classification models for aerial inspection use cases.
• Own the full loop—from dataset curation to deployment on cluster and edge targets.
• Build evaluation harnesses with per-class, per-region, and per-sensor breakdowns.
• Collaborate with the edge team to quantize and prune models for on-device inference.

Skills

PyTorch

Python

Model optimization

Tools

TensorRT

ONNX Runtime

Job description

Raad Labs is seeking a Machine Learning Engineer for aerial image classification. The role focuses on building and shipping detection and classification models across high-resolution aerial imagery, with emphasis on production-grade PyTorch pipelines and scalable deployment.

You will own dataset curation, augmentation strategies, and training infrastructure, plus deployment to clusters and edge devices. The position offers remote-first work from anywhere in the U.S. and a strong equity package.