Overview is a company that takes the cutting edge in computer vision and deep learning and applies it to previously unsolvable manufacturing inspection problems. We are truly a full stack company. We install physical cameras into the facility, run inference on the edge and manage massive deployments. Overview also streams gigabytes of video/image data to the cloud for our web platform to give customers advanced insights and analytics.

We are looking for engineers who like to solve tough challenges across the stack. This might include wrangling 500mb images or deploying optimized models in one click that can handle 10 parts a second. We are looking for people who are dynamic, who are excited to work on a different challenge every week, whether it's the final inspection on a medical device or making sure a razor blade has a perfect edge.

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

Overview's tech is far reaching. We deploy highly reliable camera hardware and GPU's into manufacturing and industrial settings. We have an IoT fleet management stack to update, secure, and manage our fleet of devices. We stream this data to our cloud or to a customers hybrid cloud where we have a user facing platform where customers can interact with their devices, deploy new deep learning models, and see advanced analytics.

We use Docker to deploy web infrastructure and deep learning models. We have an extensive deep learning stack using Tensorflow, PyTorch and packaged with Onnx. Our frontend is in React, which is updated in real time from our fleet using MQTT. And the list goes on...