Here’s a secret between you and me: even the world’s largest manufacturers, companies like Tesla and Toyota, waste billions of dollars every year making products with quality issues. Building high-quality things at scale is incredibly hard. It doesn’t just happen because you hire smart people or buy good machines. It requires seeing problems early, understanding them deeply, and acting in real time, something factories were never designed to do.

At Overview.ai, we’re changing that. We build custom hardware, edge AI, and software systems that give manufacturers real visibility into how their products are actually being made. Our technology helps catch defects earlier, reduce waste, and fundamentally improve how factories operate. This work matters, not just for our customers, but for keeping American manufacturing competitive in a world that’s moving faster every year.

We are seeking a Systems Software Engineer with strong Embedded Linux experience to join our engineering team. You will design, build, and maintain the software that powers our NVIDIA Jetson–based edge AI cameras — including Python application code, system services, OTA update mechanisms, networking, and device reliability.

This is a hands-on engineering role focused on Linux systems and product software running on resource-constrained devices. You will not be working on MCU firmware or low-level hardware bring-up. Instead, you’ll operate across the OS and application stack to ensure our camera systems are robust, secure, and easy to deploy at scale.

If you enjoy building software for real hardware , solving complex debugging challenges, and owning features end-to-end, we would love to speak with you!

What You Will Work On

• Develop and maintain system-level and application-level software for NVIDIA Jetson devices

• Implement and own OTA for our deployed device fleet

• Write Python application code for device control, edge logic, monitoring, and data flows

• Work with C/C++ components for performance-critical functionality

• Integrate camera/video pipelines to capture, process, and analyze real-time video streams

• Debug Linux systems involving multiple services, containers, and custom applications

• Tune performance across the stack: kernel, services, containers, and user applications

• Use Docker containers for packaging and deploying edge software components

• Collaborate with hardware vendors to diagnose and resolve system-level issues

• Work with backend/API teams to maintain reliable device–server communication

Qualifications

• Bachelor’s or Master’s in Computer Science, Electrical Engineering, or related field

• 5+ years of experience in Linux-based embedded systems or systems software

• 3+ years of Python development experience

• Solid C++ skills in a Linux environment

• Experience with SBC or Embedded Linux platforms

• Understanding of networking fundamentals (TCP/IP, routing, TLS/HTTPS, certificates)

• Experience debugging Linux applications and services (systemd, logs, containers)

• Strong problem-solving skills and an independent ownership mindset

• Clear communication and collaboration skills

Nice to Have

• Experience implementing OTA systems or device-update workflows

• Experience with Docker containerization

• NodeRED, Flask, or REST API development

• Industrial automation background (PLC ladder logic, Structured Text)

• Industrial protocols: EtherNet/IP, Profinet, Modbus, RS232, RS485, CANbus

• Experience with OpenCV, GStreamer, or real-time video processing

• Experience with FTP/SFTP/SMB, NTP synchronization, or device-to-server messaging

• Experience with fleet management of edge devices

We build edge AI inspection systems that run directly on GPU-enabled industrial cameras and deploy on real manufacturing lines. The hard problems are end-to-end and production-grade: designing low-latency inference pipelines, deterministic triggering and buffering, on-device data capture for fast debugging, safe/atomic updates, and fleet-scale rollout across many sites. Our team spans web developers (building local configuration and monitoring UIs that run on the camera), AI/ML engineers, embedded + firmware engineers, and AI infrastructure engineers who own training pipelines, data/versioning, deployment tooling, and reliability.