Software Engineer, Embedded Systems (AI)

TL;DR

Software Engineer, Embedded Systems (AI): Developing and optimizing high-performance software for AI-enabled radar and sensing platforms with an accent on resource-constrained edge hardware. Focus on porting AI/ML workloads, Linux kernel tuning, and building production-ready embedded systems.

Location: Hybrid in Burlington, MA. Must be legally authorized to work in the United States without sponsorship.

Compensation: $150,000–$185,000

Company

MatrixSpace develops AI-enabled radar and sensing systems that provide situational awareness in complex environments.

What you will do

• Port and optimize platform software for resource-constrained edge environments.
• Deploy, profile, and validate AI/ML-enabled applications on edge hardware.
• Develop production-quality code using C/C++, Python, and Golang.
• Collaborate with Data Science teams to integrate AI models into production pipelines.
• Work across Linux kernel, device interfaces, and system-level components.
• Participate in architecture reviews, testing, and technical planning.

Requirements

• Bachelor’s degree in a technical field or equivalent practical experience.
• Must be legally authorized to work in the US without employer sponsorship.
• Professional experience with production embedded software on edge devices.
• Strong development skills in C/C++ and working knowledge of Golang and Python.
• Strong experience with Yocto-based Linux distributions and kernel configuration.
• Strong debugging and performance optimization skills on constrained platforms.

Nice to have

• Experience deploying AI/ML models using TensorRT, ONNX Runtime, or PyTorch.
• Experience with NVIDIA Jetson, CUDA, or other edge accelerators.
• Background in radar, RF sensing, robotics, or sensor fusion.
• Experience with hardware-in-the-loop testing and board bring-up.

Culture & Benefits

• Work at the intersection of advanced sensing technology and real-world deployment.
• Collaborative multidisciplinary environment involving firmware, DevOps, and data science teams.
• Focus on solving complex systems-level challenges in a hands-on engineering culture.