Senior Edge AI Engineer & ML Platform Architect

Amatriot Group, LLC; Seattle (WA); Hybrid; USD 215,000 - 230,000; Full time

Benefits offered by this job

Hybrid work model

Job summary

Amatriot Group, LLC is seeking a Principal Machine Learning Engineer to shape the architecture and deployment of AI capabilities for counter-UAS systems. You will work with scientists and software engineers to enable multimodal sensor fusion, real-time processing, and distributed edge computing.

You will lead ML lifecycle activities from research to production, focusing on edge deployment on resource-constrained devices while ensuring performance and reliability in real-world use.

Qualifications

• 13+ years of software engineering experience.
• Proven experience architecting and maintaining large-scale distributed platforms.
• Proven experience developing, owning, and deploying machine learning models into live production products.
• Experience deploying and optimizing AI models on chips and edge devices.
• Experience with computer vision, signal processing, and machine learning infrastructure.
• Experience taking ML models from R&D to production in customer-facing software.

Responsibilities

• Develop, own, optimize, and deploy ML/DL models into production software products.
• Support scientists through AI lifecycle from research to deployment.
• Translate research outputs into scalable production AI capabilities.
• Contribute to safe, secure, and responsible AI solutions.
• Develop ML solutions involving computer vision, signal processing, and sensor fusion.
• Deploy and optimize models on edge devices and AI chips.

Skills

Python

C++

Strong programming

Edge AI

Multimodal sensor fusion

Education

B.S. in Engineering, Mathematics, Physics, or another highly technical field

Tools

PyTorch

TensorFlow

Job description

Amatriot Group, LLC is seeking a Principal Machine Learning Engineer to shape the architecture and deployment of AI capabilities for counter-UAS systems. You will work with scientists and software engineers to enable multimodal sensor fusion, real-time processing, and distributed edge computing.

You will lead ML lifecycle activities from research to production, focusing on edge deployment on resource-constrained devices while ensuring performance and reliability in real-world use.